An integrated deep learning approach for enhancing brain tumor diagnosis

The role of the pediatrician is crucial in both the diagnosis and management of pediatric brain tumors, the most common solid tumor of childhood. Awareness of the presenting signs and symptoms of brain tumors can lead to timely diagnosis, and understanding the late effects of brain tumor treatment improves long-term management of childhood brain tumor survivors.After completing this article, readers should be able to: Recognize the presenting symptoms and physical examination findings suggestive of a childhood brain tumor and how these findings depend on tumor location.Review common brain tumor pathologies affecting children.Understand how molecular genetics plays a role in the diagnosis and treatment of childhood brain tumors.Recognize the late affects associated with the treatment of childhood brain tumors.Brain tumors are the most common solid malignancy in children and represent the leading cause of pediatric cancer-related deaths. Five thousand new brain tumors are diagnosed yearly in the United States in children ages 0 to 19 years, with an incidence of approximately 6 per 100,000 children. (1) Childhood brain tumors, more than half of which are malignant, vary in terms of biology, prognosis and treatment. Presenting signs and symptoms depend on tumor location, growth rate, and presence of obstructive hydrocephalus. Making the initial diagnosis of a brain tumor can be difficult because early symptoms, such as headaches or vomiting, are nonspecific to brain tumors and more frequently are associated with other etiologies, leading to delays in diagnosis. The pediatrician plays a crucial role in the timely diagnosis of patients with brain tumors as well as recognizing late effects resulting from tumor therapies. This review summarizes the presenting features on history and physical examination, tumor classification of common tumor types, genetic brain tumor predisposition syndromes, general management strategy, and late effects of therapy.Signs and symptoms of a pediatric brain tumor can be nonspecific, insidious, intermittent, and dependent on location within the central nervous system (CNS) and the anatomical pathways affected. Although headache is the most common presenting complaint overall, it is present in only approximately one-third of the children presenting with brain tumors, and, in the absence of other symptoms or physical examination findings, is not in itself predictive of a brain tumor. Elevated intracranial pressure (ICP) is present in approximately half of all children with brain tumors. In addition to headache, it can cause nausea/vomiting, abnormalities of gait and coordination, and papilledema. Vital sign abnormalities associated with increased ICP, known as the Cushing triad (bradycardia, hypertension, abnormal respirations), are late signs of acutely increased ICP but can be absent in those with chronically elevated ICP. In young children with an open fontanelle, macrocephaly, especially when progressive, can be suggestive of hydrocephalus and a potential mass-occupying lesion. (2)Presenting symptoms depend on tumor location (Fig 1), and certain constellations of symptoms can point to specific lesion locations. Table 1 lists commonly overlooked signs and symptoms that can lead to a delayed diagnosis. Wilne et al analyzed presenting features of more than 4,000 childhood brain tumors and found that for posterior fossa tumors, three-quarters presented with nausea and vomiting, two-thirds with headache, three-fifths with abnormal gait and coordination, and one-third with papilledema. (2) In contrast, headache, nausea, and vomiting were rare in patients presenting with supratentorial tumors. Instead, seizures were present in one-third of patients, along with focal neurologic deficits such as weakness or sensory deficits on the contralateral side if there is involvement of the cortical motor or sensory regions, respectively. (2) In cases of brainstem tumors, children can present with crossed findings of ipsilateral facial weakness and contralateral hemiparesis. More than 75% of patients with brainstem tumors present with abnormal gait and coordination, whereas cranial nerve (CN) palsies are present in more than half. Headache, however, is not common in patients with brainstem tumors and is present in less than one-quarter at the time of diagnosis. Thalamic tumors can cause coordination and motor difficulties or hemiplegia. (2)Patients with pituitary tumors or optic pathway tumors often present with visual deficits. It is not uncommon for even severe visual deficits in children to go unrecognized by the patient, parents, or pediatrician. (3) Because patients with neurofibromatosis (NF) type 1 are at increased risk for optic pathway glioma, they should have yearly ophthalmology evaluations. Children with pituitary or hypothalamic tumors often present with endocrine abnormalities, such as failure to thrive, excessive thirst, or central obesity.Children with spinal cord tumors most commonly present with back pain, present at diagnosis in approximately two-thirds of cases. Spinal cord tumors may occur in extradural, intramedullary, and extramedullary intradural locations. Although some children may present with scoliosis, most will not. Spinal cord compression causes signs such as gait and coordination abnormalities, focal weakness, or bowel and bladder dysfunction. (2)A comprehensive neurologic examination (summarized in Table 2) is crucial to identify abnormalities that might be suggestive of a CNS tumor. A normal neurologic examination does not exclude the diagnosis of a brain or spinal cord tumor and must be correlated with symptoms.Patients with acute hydrocephalus can display dramatic changes in their mental status, with increased sleepiness, decreased energy, and decreased responsiveness. However, those with chronic hydrocephalus might show only subtle signs, such as slowly declining school performance.A fundoscopic examination of the optic nerve, CN II, is crucial to assess for papilledema and optic nerve pallor, which can reveal information about hydrocephalus or tumors along the optic pathways. A fundoscopic examination can be difficult in young or uncooperative children, warranting referral to ophthalmology for a dilated examination. Vision should be assessed by confrontation in the 4 quadrants of each eye because different patterns of visual field deficits will suggest varying tumor locations. In younger children, assessment of visual fields can be performed using a colorful object for central fixation and introducing a second object in the periphery and watching for the eyes to track to that object.Eye movements are controlled by CNs III, IV, and VI. The nuclei of CNs III and IV are located in the midbrain, whereas the nucleus of CN VI is in the pons, and brainstem tumors can lead to abnormalities of extraocular movements. Large pineal tumors can cause Parinaud syndrome, characterized by upgaze palsy, convergence-retraction nystagmus, and poorly reactive pupils due to compression of the rostral midbrain. Nystagmus can also be seen in patients with cerebellar tumors or optic pathway tumors.CN V, the trigeminal nerve, has 3 divisions that give sensation to the face. The trigeminal nucleus is located in the pons, as is the nucleus of CN VII (the facial nerve), which controls facial movement. Facial asymmetry or decreased facial sensation should raise concern for a mass in this region. Hearing in each ear should be assessed to look for CN VIII dysfunction.The lower CNs (CNs IX, X, XII) exit from the medulla and are involved in phonation, swallowing, and tongue movement. Palatal asymmetry, change in voice quality, or unilateral glossal atrophy raises suspicion for a medullary lesion. CN XI, the accessory nerve, has the most distal nucleus, also in the medulla, and innervates the trapezius and sternocleidomastoid musculature.Motor function, sensation, and reflexes should be assessed with special attention to comparison with the contralateral findings. Asymmetry can indicate a lesion affecting corticospinal tracts (motor), spinothalamic tracts (temperature, pain, light touch), or dorsal columns (proprioception, vibratory sense). Asymmetrical hyporeflexia can indicate lower motor neuron injury, whereas hyperreflexia and the presence of a Babinski reflex are indicative of upper motor neuron dysfunction. In acute upper motor neuron injury, reflexes may be absent.Patients with cerebellar tumors can present with a wide-based ataxic gait and difficulty with tandem gait. A hemiparetic gait can suggest a tumor involving cortical motor areas, the thalamus, or the brain stem. Patients with cerebellar or brainstem tumors may exhibit abnormal coordination, elicited by testing rapid alternating movements, finger to nose testing, or finger (pointer to thumb) and toe tapping (on the floor) or asking a child to mirror the examiner's finger as the examiner moves the finger laterally and/or vertically.Although not technically part of the neurologic examination, a skin examination is important to assess for dermatologic manifestations of underlying tumor predispositions such as NF type 1 (predisposed to low-grade gliomas [LGGs], especially in optic pathways), NF type 2 (predisposed to acoustic schwannomas and meningiomas), tuberous sclerosis complex (predisposed to subependymal giant cell tumors), or, more rarely, constitutional mismatch repair deficiency syndrome. Patients with constitutional mismatch repair deficiency syndrome have a genetic defect in genes responsible for repairing a specific type of DNA damage known as mismatch repair. Abnormalities in these genes (MLH1, MSH2, MSH5, PMS2) make it more difficult for the body to repair normally occurring DNA damage, leading to mutations and predisposing these patients to many types of cancers at an early age, including brain tumors, most commonly high-grade gliomas (HGGs). (4)The child with a suspected brain tumor might require urgent interventions. Those with unstable vital signs, altered mental status, or concern for increased ICP warrant expedited evaluation, best managed initially in the emergency department. Although magnetic resonance imaging (MRI) with and without contrast is the gold standard imaging technique for optimal visualization for brain tumors and is often needed for neurosurgical planning, in the unstable child, a computed tomographic (CT) scan may be the best initial imaging choice. CT scans can provide information regarding acute hydrocephalus, impending herniation, or acute hemorrhage, all of which represent neurosurgical emergencies. They can also show the anatomical location of a mass, lesion size, presence of hydrocephalus, and whether the mass is compressing other brain structures, thereby helping to triage and plan a timeline for MRI, surgery, or other sedated procedures. When choosing the optimal initial imaging study for a young child who would require anesthesia to complete an MRI, the relative risks of anesthesia compared with the risk of exposure to ionizing radiation from a CT scan, which could be completed without sedation, must be weighed while taking into account the degree of suspicion for an abnormality and individual risk factors specific to that patient. (5)MRI with and without contrast is generally the preferred imaging modality for diagnosis and follow-up of brain tumors. MRI allows for more detailed characterization of the tumor itself and the surrounding anatomy, with more specialized sequences for visualization of edema, relationship to CNs, blood vessels, and perfusion. Furthermore, MRI does not expose children to ionizing radiation so is preferred over CT for repeated studies, as would be needed to follow a brain tumor. Most patients with a brain tumor require a spinal MRI to evaluate for evidence of leptomeningeal disease.When a diagnosis of a brain tumor is made based on imaging, in the absence of a neurosurgical emergency, patients should be managed in concert with neuro-oncology teams preoperatively. Early neuro-oncology consultation allows for additional baseline neurologic examination, can help inform surgical planning based on the working differential diagnosis and postoperative treatment options, and facilitates an opportunity for clinical trial enrollment where presurgical consent may be required.The care of the pediatric neuro-oncology patient requires a multidisciplinary team–based approach. In addition to an excellent primary care pediatrician, this team includes neuro-oncology, neuro-surgery, neurology, neuro-radiology, radiation oncology, genetics, endocrinology, ophthalmology, audiology, neuropsychology, physical medicine and rehabilitation, palliative care, and social work.Upfront treatment of pediatric brain tumors generally includes surgery, radiotherapy, chemotherapy, or a combination of these modalities. For most tumor types, maximal safe surgical resection is pursued to obtain diagnosis and as the first step in definitive treatment. Some notable exceptions to this include tumors in eloquent locations where resection would result in significant morbidity or mortality. These locations include the brain stem, optic pathways, thalamus, internal capsule, sensory and motor cortices, visual cortex, or Broca and Wernicke areas, which are important for receptive and expressive language. In some cases, a small needle biopsy of these areas can be performed to obtain tissue for diagnostic purposes. For germ cell tumors, tumor markers can be diagnostic, obviating the need for upfront surgery. Some patients with low-grade–appearing lesions are followed with observation alone.Although some low-grade tumors can be treated with resection only, many low-grade and most high-grade tumors require additional postsurgical treatment. The standard of care for postsurgical management of pediatric brain tumors is constantly evolving based on emerging preclinical and clinical data. In many cases, enrollment in an open clinical trial is considered the standard of care. There are a variety of clinical trial consortia and cooperative groups with open protocols focused on pediatric brain tumors. A complete list of open clinical trials can be found on clinicaltrials.gov.There are more than 30 unique pathologies of CNS tumors in children. MRI characteristics of some common childhood brain tumors are shown in Fig 2. The advent of molecular genetics has enhanced our understanding of the biologic behavior of brain tumors, has changed tumor classification systems, and has had treatment implications.Medulloblastoma is the most common malignant brain tumor in children and is of embryonal origin. It generally presents as a posterior fossa mass and, due to its location, is often associated with obstructive hydrocephalus. Staging includes an MRI of the spine and a lumbar puncture looking for malignant cells in the cerebrospinal fluid (CSF). Histologically it is classified as classic, large cell anaplastic, or nodular desmoplastic. Overall, medulloblastoma has 5-year overall survival (OS) of approximately 70%. (6)Treatment depends on age at presentation, extent of resection, and presence of metastatic disease. Recent trials are accounting for molecular subtype in treatment decisions. Generally, treatment involves maximal tumor resection, craniospinal radiotherapy, and chemotherapy. Young patients undergo high-dose chemotherapy with autologous stem cell rescue to avoid or delay irradiation.Medulloblastoma has been classified into 4 principle molecular subgroups: WNT (wingless), SHH (sonic hedgehog), group 3, and group 4 (Table 3). (7) WNT-driven medulloblastomas are rarely metastatic and have the best overall prognosis, with greater than 90% OS. Current clinical trials are focused on reducing therapy in this subtype. SHH-driven tumors have a bimodal distribution presenting most commonly in infants or adolescents and young adults. They have an intermediate prognosis, although association with p53 mutations portends a poor prognosis. (9) Group 3 and group 4 tumors are known as non-WNT, non-SHH medulloblastoma subtypes. Although immunohistochemical studies can differentiate WNT and SHH medulloblastoma from the non-WNT and non-SHH medulloblastoma subtypes, other molecular methods, such as methylation studies, are needed to distinguish group 3 from group 4 tumors. Group 3 tumors can present in very young children, often have MYC amplification, are commonly metastatic at presentation, and have the poorest outcomes overall of any subgroup. Recent data suggest that group 3 tumors might benefit from intensified chemotherapy concurrent with radiotherapy. Group 4 tumors are the most common subgroup overall, presenting in children and adults and, similar to group 3 tumors, more commonly present in males than in females. (7) Group 4 tumors have an intermediate prognosis.Atypical teratoid rhabdoid tumors (ATRTs) are also embryonal tumors but can present in the posterior fossa or supratentorial region. These tumors have a very poor prognosis, with 3-year OS of approximately 25%. Survival trends improve with older age at diagnosis, with those older than 3 years faring better than younger patients. (10) Histologically, the loss of INI1, encoded by SMARCB1, is pathognomonic. Up to 35% of patients with ATRT have a germline mutation in SMARCB1 (or rarely SMARCA4), which predisposes them to the development of malignant rhabdoid tumors in other locations, most commonly the kidneys. Germline variants are more common in younger patients, and approximately two-thirds are sporadic. (11)Staging includes MRI of the brain and spine and lumbar puncture for CSF cytology. Treatment involves surgical resection, radiotherapy, and chemotherapy, with or without triple tandem autologous stem cell transplant. Recent clinical trial data showed improved survival outcomes compared with historical controls achieved with a regimen including radiotherapy for patients as young as 6 months and 3 cycles of high-dose chemotherapy with autologous stem cell rescue for all patients. (12) A meta-analysis including 130 patients with ATRT saw that survival correlated most strongly when patients were treated with regimens that included high-dose chemotherapy with autologous stem cell rescue. Treatment modalities of radiotherapy and intrathecal chemotherapy also lead to a statistically significant improvement in OS in this cohort. (10)ATRT tumors have also been classified based on molecular characteristics into 3 subgroups: ATRT–tyrosine (ATRT-TYR), ATRT–sonic hedgehog (ATRT-SHH), and ATRT–myelocytomatosis oncogene (ATRT-MYC), but further research is needed to delineate the prognostic and clinical implications of these subgroups. (13)Ependymoma represents the third most common brain tumor in children and arises from the ependymal cells lining the ventricles or the central canal of the spinal cord. Two-thirds of ependymomas present in the posterior fossa, with the remainder in the supratentorial region or spinal cord. For pediatric ependymoma as a whole, OS at 10 years is approximately 64%, but cases achieving gross total resection followed by radiotherapy fare significantly better. Molecular subtype and gain of chromosome 1q has important prognostic implications as well. (14)Ependymoma is treated with maximal surgical resection followed by focal radiotherapy, except for spinal disease, in which gross total resection without adjuvant radiotherapy can be curative. The role of chemotherapy in ependymoma remains under clinical investigation. Studies have also explored the use of postoperative chemotherapy to delay or omit radiotherapy in patients younger than 3 years, but outcomes were inferior to regimens involving radiotherapy for children older than 12 months. (15)Ependymoma has been divided into 9 molecular subgroups, with 3 subgroups for each anatomical location: spinal, supratentorial, and posterior fossa. Only 6 of the molecular subtypes generally affect children. Pediatric ependymoma of the spine is divided into the subtype and the subtype spinal subtypes have a prognosis. In the posterior fossa, patients with have a prognosis than those with and in the supratentorial those with have OS than those with and are associated with OS less than and survival of approximately are a group of tumors that including tumors the most tumors as and tumors When represent the most common brain tumor in children and can present in many anatomical locations. are less to to other of the CNS than their malignant and in some cases gross total resection can be curative. However, resection is not in certain anatomical locations, such as in the brain stem or with optic pathway common in patients with NF type have a prognosis, with OS of and survival of in a study of with follow-up of when therapy is needed for the regimen of chemotherapy with or and although other chemotherapy regimens have as well. is not in the upfront management of due to for late of the molecular of has that most are by in the most commonly variants and NF type 1 have shown pediatric and have shown in tumors. contrast to pediatric have a prognosis. include high-grade tumors brainstem tumors and of pediatric is tumors may be to surgical is followed by radiotherapy and chemotherapy for these tumors, as a Group study showed improved survival when chemotherapy to radiotherapy compared with radiotherapy specific chemotherapy regimen has as a standard of care for upfront pediatric In contrast, for tumors such as glioma, chemotherapy to radiotherapy has not been shown to survival the to OS and the OS achieved with radiotherapy open based and clinical trials are patients, to improve outcomes for these patients. studies in pediatric that the of pediatric from that of mutations and in tumors, and in tumors, the of in pediatric and a poor prognosis. are from in older children, with significantly improved are more common in children younger than 1 and under are germ cell tumors represent approximately of pediatric brain tumors and are as and germ cell tumors They most commonly in the pineal region but can also present in the thalamus, or or or both or which can be in blood and/or can cause of in the CSF but not In some cases, diagnosis can be made based on CSF and tumor whereas biopsy is when tumor markers are have a better overall prognosis, with OS greater than 90% compared with to for are commonly treated with 4 cycles of chemotherapy followed by radiotherapy to the tumor and whereas are generally treated with 6 cycles of chemotherapy alternating with and craniospinal radiotherapy in many cases, although studies are whether radiotherapy can be in patients with to associated with craniospinal radiotherapy. is a tumor from the of the and solid Histologically, they are classified as tumors and are divided into and subtypes. to their location they can and treatment for is some a more neurosurgical in an to avoid radiotherapy, and an initial resection followed by upfront radiotherapy. are germline mutations that children to specific types of childhood brain tumors in the of tumor predisposition of these is important to the primary care who follow these patients In the child who presents with a brain especially in the of other history of tumors, history of tumors at a young age, or dermatologic findings, it is important to further for these predisposition Children with a known history of predisposition might require genetic for the presence of these syndromes, and specific tumor if found to of these Furthermore, the presence of certain underlying may the of therapy for the management of a brain tumor. Table 4 summarizes germline associated with specific brain tumor the treatment of different tumor types each of the commonly treatment modalities their risks and acute risks of include and damage to structures, as well as morbidity dependent on tumor For posterior fossa syndrome affects an to of patients who undergo resection of large posterior fossa tumors. fossa syndrome is characterized by a combination of or significant with and or motor occurring within 2 of cerebellar and symptoms can months to and many are with deficits. Patients with supratentorial tumors are at greater risk for postoperative seizures and are often on Children with tumors are at increased risk for postoperative visual deficits and tumors by or at a tumor to damage is over to a total to the are without mass, that when at a point to a certain they also radiation at a lower on both the and exit side of the have mass, so the radiation is to within the the of at that the that the effects of radiotherapy are due to the radiation in both and radiotherapy, patients can skin which generally over the treatment Patients intracranial radiotherapy often headache or radiotherapy can cause due to the by the body and can the growth of the resulting in loss of in younger radiotherapy is preferred over radiotherapy, for patients who require craniospinal radiotherapy, because it to important such as the and For patients focal radiotherapy, therapy may to important or result in a significantly overall radiation on tumor the radiation field and for a plan a plan can be to evaluate relative of over based on the brain that would a with each to the use of therapy are the of radiotherapy

Brain tumors, being the most severe and complex kind of cancer, necessitate specialized investigation for diagnosis, treatment, and care. Early recognition of brain tumors enhances patient care and reduces mortality rates. The application of deep learning in MRI diagnostics has transformed medicine. The study employs real and synthetic MRI data to evaluate novel deep-learning models to enhance brain tumor diagnosis. The ensemble model employed AlexNet, VGG16, and ResNet 18 on MR data from Rizgary Hospital in Erbil and Hiwa Hospital in Sulemani, as well as synthetic images produced by Deep Convolutional Generative Adversarial Networks. Modeling measurements encompassed accuracy, precision, recall, and F1 score. The architecture of ResNet18 and its capacity to incorporate residual connections for feature mapping enabled it to surpass all other models in classification accuracy, achieving 99%. Although AlexNet and VGG16 achieve accuracies of 98.16% and 98.83%, respectively, ResNet18 excels in differentiating between normal and unusual instances. DCGANs excel in generating synthetic images and enhancing image categorization and model precision. A study utilizing both real and synthetic images showed that synergistic virtual paradigms could enhance the accuracy of clinical instruments and facilitate deeper AI integration in medicine. Subsequent research will focus on optimizing model architecture and implementing data augmentation techniques to enhance classification accuracy. This Python study demonstrates that deep learning can enhance the diagnosis and treatment of brain tumors.

Brain tumors are serious health problems that must be diagnosed accurately and in a timely manner in order to provide effective treatment. Magnetic resonance imaging (MRI) is widely used in the detection of brain tumors. The accuracy of MRI results depends on the expertise of the physician and usually requires confirmation with biopsy. In recent years, revolutionary developments in image processing and deep learning technologies have provided significant improvements in the diagnosis and classification of brain tumors using MRI. In this study, it is aimed to classify brain tumors accurately and effectively for four different classes (glioma, meningioma, pituitary, and no tumor) previously created using MRI image data. Four different transfer learning-based deep learning methods for classification; ResNet-18, EfficientNet-B0, DenseNet-121, and ConvNeXt-Tiny, are compared using the Fastai library. Accurate diagnosis of brain tumors is of critical importance in the treatment of patients, and the aim of the study is to achieve high accuracy and speed. Our proposed Fastai library-based EfficientNet-B0 model has achieved both fast and highly successful results in the diagnosis of brain tumors with a 99% accuracy rate and 73 minutes of training performance. In addition, the DenseNet-121 model has achieved highly successful results with 99% accuracy rates, and the ResNet-18 and ConvNeXt-Tiny models have achieved 98% accuracy rates. Our results provide fast and effective insights into the possible uses of deep learning frameworks in the field of medical imaging. In addition, these results provide significant improvements compared to studies in the literature.

Patients suffering from brain tumors have significant rate of mortality. The diagnosis of brain tumors, if carried out erroneously, may result in incorrect medical intervention and hence may reduce the chance of survival of the patient. Since the risk of developing brain tumors increases with age and as there has been an increase in the aging population in the world, an urgent need is felt to develop simple and low cost analytical tools for its early diagnosis. Usually, MRI scans are used to image a patient’s brain. In recent times, machine learning and its sub-domain, deep learning has reduced the need for human judgment in the diagnosis of diseases. Deep learning models are increasingly being adopted in lieu of traditional supervised learning algorithms due to their inherent advantages owing to their ability to gather requisite details from the images automatically. One of the most difficult bio-medical imaging problems is to detect whether a patient has developed brain tumor. Despite having literatures dedicated to detect or classify various types of brain tumors through deep learning approaches, they lack high accuracy. Here, a solution for the classification of two types of brain tumors, namely Meningioma and Glioma is presented. A novel 13-layer deep convolutional neural network (CNN) architecture is used, that is built from scratch. The proposed system after performing 10-fold cross-validation gives an average validation accuracy of 100%. It is the highest attainable accuracy among existing works performed on axial MRIs, and on the same dataset.

There are minimal published data on the relationship between personality traits and learning approaches among medical students. This study explored the causal-effect relationship of personality traits and learning approaches among medical students. A cross-sectional study was conducted on medical students and they responded to the Learning Approach Inventory and USM Personality Inventory to measure personality traits and learning approaches, respectively. A structural equation modelling was performed by AMOS 24 to test the causal-effect relationship of personality traits and learning approaches. Conscientiousness had a positive direct effect on deep learning approach, while neuroticism had negative direct effect on deep and strategic learning approaches. Extraversion, openness, and agreeableness had no significant link or effect on any learning approaches. Strategic learning approach had positive direct effect on deep learning approach and a mediator for surface learners on deep learning approach. Surface learning approach had a negative direct effect on deep learning approach. There was a significant relationship of specific personality traits and learning approaches. Conscientiousness and neuroticism had significant relationships with deep and strategic learning approaches. These findings enables medical educators to have a better understanding of the influence of personality traits on medical students’ learning approaches to learning tasks and their implications on instructional strategies.

Investigating the learning approaches of students in nursing education

The article reflects on the classification of brain tumors where several deep learning (DL) approaches are used. Both primary and secondary brain tumors reduce the patient’s quality of life, and therefore, any sign of the tumor should be treated immediately for adequate response and survival rates. DL, especially in the diagnosis of brain tumors using MRI and CT scans, has applied its abilities to identify excellent patterns. The proposed ensemble framework begins with the image preprocessing of the brain MRI to enhance the quality of images. These images are then utilized to train seven DL models and all of these models recognize the features related to the tumor. There are four models which are General, Glioma, Meningioma, and Pituitary tumors or No Tumor model, which helps in reaching a joint profitable prediction and concentrating solely on the strength of the estimation and outcome. This is a significant improvement over all the individual models, attaining a 99. 43% accuracy. The data used in this research was gotten from Kaggle website and comprised of 7023 images belonging to four classes. Future work will focus on increasing the dataset size, investigating additional DL architectures, and enhancing real-time detection to improve the accuracy of diagnostic scans and their overall relevance to clinical practice.

A brain tumor, a disease that can be fatal, threatens the most valuable human life, and it is a difficult work for a doctor to diagnose the tumor accurately and promptly. The aberrant proliferation of brain cells results in a condition known as a brain tumor. The rarity and variety of tumors make it difficult to gauge a patient’s prognosis after being diagnosed with one. Manual identification is a time-consuming and difficult method that can lead to inaccuracies in the results of tumor identification using Magnetic Resonance Imaging (MRI). MRI images play a vital role in tumor site determination. These limits necessitate the use of computer-assisted techniques. It is common practice to utilize MRI scans to identify a variety of tissue abnormalities, to look for tumors, and to assess whether a tumor is still present or returning. Deep learning (DL) algorithms are being utilized in neuroimaging to detect brain cancers using MR images as artificial intelligence advances. It is critical that medical photographs be processed to aid in the identification of various disorders. The information and expertise of the physician are critical in the diagnosis of brain tumors. Physicians need an automated method to detect and classify brain cancers. MRI-based segmentation of brain approaches will be reviewed in this research. Deep learning methods for automatic segmentation have recently gained popularity since they produce cutting-edge results and are more suited to dealing with this challenge. It is also possible to use deep learning approaches to analyses and evaluate vast quantities of MRI-based image data quickly and objectively. There is a slew of review studies about classic MRI-based approaches for classifying brain tumor pictures. Deep learning approaches were used to classify brain cancers as glioma, meningioma, or pituitary. Conclusions and future advances are also discussed in this section to ensure that MRI-based tumor segmentation methods can be implemented in daily practice.

An abnormal growth of cells in the brain, often known as a brain tumor, has the potential to develop into cancer. Carcinogenesis of glial cells in the brain and spinal cord is the root cause of gliomas, which are the most prevalent type of primary brain tumor. After receiving a diagnosis of glioblastoma, it is anticipated that the average patient will have a survival time of less than 14 months. Magnetic resonance imaging (MRI) is a well-known non-invasive imaging technology that can detect brain tumors and gives a variety of tissue contrasts in each imaging modality. Until recently, only neuroradiologists were capable of performing the tedious and time-consuming task of manually segmenting and analyzing structural MRI scans of brain tumors. This was because neuroradiologists have specialized training in this area. The development of comprehensive and automatic segmentation methods for brain tumors will have a significant impact on both the diagnosis and treatment of brain tumors. It is now possible to recognize tumors in photographs because of developments in computer-aided design (CAD), machine learning (ML), and deep learning (DL) approaches. The purpose of this study is to develop, through the application of MRI data, an automated model for the detection and classification of brain tumors based on deep learning (DLBTDC-MRI). Using the DLBTDC-MRI method, brain tumors can be detected and characterized at various stages of their progression. Preprocessing, segmentation, feature extraction, and classification are all included in the DLBTDC-MRI methodology that is supplied. The use of adaptive fuzzy filtering, often known as AFF, as a preprocessing technique for photos, results in less noise and higher-quality MRI scans. A method referred to as “chicken swarm optimization” (CSO) was used to segment MRI images. This method utilizes Tsallis entropy-based image segmentation to locate parts of the brain that have been injured. In addition to this, a Residual Network (ResNet) that combines handcrafted features with deep features was used to produce a meaningful collection of feature vectors. A classifier developed by combining DLBTDC-MRI and CSO can finally be used to diagnose brain tumors. To assess the enhanced performance of brain tumor categorization, a large number of simulations were run on the BRATS 2015 dataset. It would appear, based on the findings of these trials, that the DLBTDC-MRI method is superior to other contemporary procedures in many respects.

Background: Brain tumors constitute a critical and potentially life-threatening condition that requires accurate and timely diagnosis. In recent years, machine learning (ML) approaches have emerged as powerful tools for assisting radiologists and clinicians in identifying and classifying tumors on medical imaging. While numerous ML methods, including conventional machine learning algorithms and deep learning-based models, have been explored, a comprehensive analysis of their efficacy in detecting, segmenting, and classifying brain tumors remains essential. Methods: We conducted a systematic evaluation of diverse ML methods used in brain tumor diagnosis. We first identified and collated relevant studies from major scientific databases, focusing on image-based applications such as magnetic resonance imaging (MRI). The methods included pre-processing pipelines, feature extraction techniques, and both supervised and unsupervised learning approaches. We then compared these methods based on standard metrics, including accuracy, sensitivity, specificity, and F1-score, to gain insights into their relative performance. Furthermore, we applied representative algorithms (such as support vector machines, convolutional neural networks, and random forests) to an open-access brain tumor imaging dataset to evaluate their performance and compare empirical findings. Results: Results indicated that deep learning models, particularly convolutional neural networks, consistently outperformed traditional ML models across various performance metrics. In particular, automated feature extraction in deep learning approaches proved pivotal in capturing nuanced information such as tumor shape and heterogeneity. Conventional algorithms still demonstrated merit when dataset sizes were limited or when computational resources were constrained. Additionally, various data augmentation techniques improved the robustness and generalizability of ML models in situations with scarce annotated data. Conclusion: Our findings suggest that deep learning-based methods hold the greatest potential for accurate and efficient brain tumor diagnosis, particularly when coupled with robust datasets and high-quality imaging. Nonetheless, careful selection of model architecture, training strategies, and validation protocols remains paramount to ensure reliable clinical translation.

This study aimed to compare the impact of the partially flipped physiology classroom (PFC) and the traditional lecture-based classroom (TLC) on students' learning approaches. The study was conducted over 5 mo at Xiangya School of Medicine from February to July 2022 and comprised 71 students majoring in clinical medicine. The experimental group (n = 32) received PFC teaching, whereas the control group (n = 39) received TLC. The Revised Two-Factor Study Process Questionnaire (R-SPQ-2F) was used to assess the impact of different teaching methods on students' learning approaches. After the PFC, students got significantly higher scores on deep learning approach (Z = -3.133, P < 0.05). Conversely, after the TLC students showed significantly higher scores on surface learning approach (Z = -2.259, P < 0.05). After the course, students in the PFC group scored significantly higher in deep learning strategy than those in the TLC group (Z = -2.196, P < 0.05). The PFC model had a positive impact on deep learning motive and strategy, leading to an improvement in the deep approach, which is beneficial for the long-term development of students. In contrast, the TLC model only improved the surface learning approach. The study implies that educators should consider implementing PFC to enhance students' learning approaches.NEW & NOTEWORTHY In this article, we compare the impact of the partially flipped classroom (PFC) and the traditional lecture classroom (TLC) in a physiology course on medical students' learning approaches. We found that the PFC benefited students by significantly enhancing their deep learning motive, strategy, and approach, which was good for them. However, the TLC model only improved the surface learning motive and approach.

Person re-identification (ReID) aims to find the person of interest across multiple non-overlapping cameras. It is considered an essential step for person tracking applications which is vital for surveillance. Person ReID could be investigated either using image-based or video-based. Video-based person ReID is considered more discriminating and realistic than image-based ReID due to the massive information extracted for each person. Different deep-learning techniques have been used for video-based ReID. In this survey, recently published articles are reviewed according to video-based ReID system pipeline: deep features learning, deep metric learning, and deep learning approaches. The deep feature learning approaches are categorized into spatial and temporal approaches, while deep metric learning is divided into metric and metric learning approaches. The deep learning approaches are differentiated into: supervised, unsupervised, weakly-supervised, and one-shot learning. A detailed analysis is held for the architectures of the state-of-the-art deep learning approaches. And their performance on four benchmark datasets is compared.

We describe the sentiment analysis experiments that were performed on the Lithuanian Internet comment dataset using traditional machine learning (Naïve Bayes Multinomial—NBM and Support Vector Machine—SVM) and deep learning (Long Short-Term Memory—LSTM and Convolutional Neural Network—CNN) approaches. The traditional machine learning techniques were used with the features based on the lexical, morphological, and character information. The deep learning approaches were applied on the top of two types of word embeddings (Vord2Vec continuous bag-of-words with negative sampling and FastText). Both traditional and deep learning approaches had to solve the positive/negative/neutral sentiment classification task on the balanced and full dataset versions. The best deep learning results (reaching 0.706 of accuracy) were achieved on the full dataset with CNN applied on top of the FastText embeddings, replaced emoticons, and eliminated diacritics. The traditional machine learning approaches demonstrated the best performance (0.735 of accuracy) on the full dataset with the NBM method, replaced emoticons, restored diacritics, and lemma unigrams as features. Although traditional machine learning approaches were superior when compared to the deep learning methods; deep learning demonstrated good results when applied on the small datasets.

INTRODUCTION: A new diagnosis of a brain tumor in a child has significant emotional consequences for every member of the family. Nevertheless, early phases of oncologic care rarely provide formal mental health screening. METHODS: We implemented a mental health screening protocol for families and patients with a newly diagnosed brain tumor admitted to our pediatric intensive care unit (PICU) at the time of diagnosis. Screening instruments were selected based on their previous validation and relevance to both a brain tumor diagnosis and PICU admission. Parents were contacted by a member of our team within 2 weeks of their child’s diagnosis, and completed the screening independently within the next 2 weeks via an online interface. Parent proxy reports for the children were used when necessary. Scores were shared with the family and neuro-oncology team. When indicated, supportive counseling was offered. RESULTS: Eighteen patients have met criteria for mental health screening. Of these, 8 patient and parent dyads have completed the screening (mean patient age 8.6 years; 75% white; 50% female). Of the families that completed the evaluation, most parents (62.5%) reported that their child’s diagnosis negatively impacted their health-related quality of life (HRQL), while 87.5% of children themselves reported a lower HRQL. Most children (66.7%) self-reported having symptoms outside of normal range for anger, anxiety and depression and 100% had scores outside of normal range for pain interference with their daily lives. Only half of eligible families accepted referrals for new mental health support. CONCLUSION: Both children with a new diagnosis of brain tumor and their parents are at risk for impaired mental health and quality of life early after diagnosis. A systematic approach to these concerns at the time of diagnosis may be helpful.

Diagnosis of Brain tumor at an early stage has became an important topic of research in recent time. Detection of tumor at an early stage for primary treatment increases the patient’s survival rate. Processing of Magnetic resonance image (MRI) for an early tumor detection face the challenge of high processing overhead due to large volume of image input to the processing system. This result to large delay and decrease in system efficiency. Hence, the need of an enhanced detection system for accurate segmentation and representation for a faster and accurate processing has evolved in recent past. Development of new approaches based on improved learning and processing for brain tumor detection has been proposed in recent literatures. This paper outlines a brief review on the developments made in the area of MRI processing for an early diagnosis and detection of brain tumor for segmentation, representation and applying new machine learning (ML) methods in decision making. The learning ability and fine processing of Machine learning algorithms has shown an improvement in the current automation systems for faster and more accurate processing for brain tumor detection. The current trends in the automation of brain tumor detection, advantages, limitations and the future perspective of existing methods for computer aided diagnosis in brain tumor detection is outlined.