Different Types Of Brain Tumors Research Articles

Efficient‐Residual Net—A Hybrid Neural Network for Automated Brain Tumor Detection

ABSTRACTA multiscale feature fusion of Efficient‐Residual Net is proposed for classifying tumors on brain Magnetic resonance images with solid or cystic masses, inadequate borders, unpredictable cystic and necrotic regions, and variable heterogeneity. Therefore, in this research, Efficient‐Residual Net is proposed by efficaciously amalgamating features of two Deep Convolution Neural Networks—ResNet50 and EffficientNetB0. The skip connections in ResNet50 have reduced the chances of vanishing gradient and overfitting problems considerably thus learning of a higher number of features from input MR images. In addition, EffficientNetB0 uses a compound scaling coefficient for uniformly scaling the dimensions of the network such as depth, width, and resolution. The hybrid model has improved classification results on brain tumors with similar morphology and is tested on three internet repository datasets, namely, Kaggle, BraTS 2018, BraTS 2021, and real‐time dataset from Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh. It is observed that the proposed system delivers an overall accuracy of 96.40%, 97.59%, 97.75%, and 97.99% on the four datasets, respectively. The proposed hybrid methodology has given assuring results of 98%–99% of other statistical such parameters as precision, negatively predicted values, and F1 score. The cloud‐based web page is also created using the Django framework in Python programming language for accurate prediction and classification of different types of brain tumors.

The Role of ArtificiaI Intelligence in Brain Tumor Diagnosis: An Evaluation of a Machine Learning Model.

This research study explores of the effectiveness of a machine learning image classification model in the accurate identification of various types of brain tumors. The types of tumors under consideration in this study are gliomas, meningiomas, and pituitary tumors. These are some of the most common types of brain tumors and pose significant challenges in terms of accurate diagnosis and treatment. The machine learning model that is the focus of this study is built on the Google Teachable Machine platform (Alphabet Inc., Mountain View, CA). The Google Teachable Machine is a machine learning image classification platform that is built from Tensorflow, a popular open-source platform for machine learning. The Google Teachable Machine model was specifically evaluated for its ability to differentiate between normal brains and the aforementioned types of tumors in MRI images. MRI images are a common tool in the diagnosis of brain tumors, but the challenge lies in the accurate classification of the tumors. This is where the machine learning model comes into play. The model is trained to recognize patterns in the MRI images that correspond to the different types of tumors. The performance of the machine learning model was assessed using several metrics. These include precision, recall, and F1 score. These metrics were generated from a confusion matrix analysis and performance graphs. A confusion matrix is a table that is often used to describe the performance of a classification model. Precision is a measure of the model's ability to correctly identify positive instances among all instances it identified as positive. Recall, on the other hand, measures the model's ability to correctly identify positive instances among all actual positive instances. The F1 score is a measure that combines precision and recall providing a single metric for model performance. The results of the study were promising. The Google Teachable Machine model demonstrated high performance, with accuracy, precision, recall, and F1 scores ranging between 0.84 and 1.00. This suggests that the model is highly effective in accurately classifying the different types of brain tumors. This study provides insights into the potential of machine learning models in the accurate classification of brain tumors. The findings of this study lay the groundwork for further research in this area and have implications for the diagnosis and treatment of brain tumors. The study also highlights the potential of machine learning in enhancing the field of medical imaging and diagnosis. With the increasing complexity and volume of medical data, machine learning models like the one evaluated in this study could play a crucial role in improving the accuracy and efficiency of diagnoses. Furthermore, the study underscores the importance of continued research and development in this field to further refine these models and overcome any potential limitations or challenges. Overall, the study contributes to the field of medical imaging and machine learning and sets the stage for future research and advancements in this area.

Integrated approach of federated learning with transfer learning for classification and diagnosis of brain tumor

Brain tumor classification using MRI images is a crucial yet challenging task in medical imaging. Accurate diagnosis is vital for effective treatment planning but is often hindered by the complex nature of tumor morphology and variations in imaging. Traditional methodologies primarily rely on manual interpretation of MRI images, supplemented by conventional machine learning techniques. These approaches often lack the robustness and scalability needed for precise and automated tumor classification. The major limitations include a high degree of manual intervention, potential for human error, limited ability to handle large datasets, and lack of generalizability to diverse tumor types and imaging conditions.To address these challenges, we propose a federated learning-based deep learning model that leverages the power of Convolutional Neural Networks (CNN) for automated and accurate brain tumor classification. This innovative approach not only emphasizes the use of a modified VGG16 architecture optimized for brain MRI images but also highlights the significance of federated learning and transfer learning in the medical imaging domain. Federated learning enables decentralized model training across multiple clients without compromising data privacy, addressing the critical need for confidentiality in medical data handling. This model architecture benefits from the transfer learning technique by utilizing a pre-trained CNN, which significantly enhances its ability to classify brain tumors accurately by leveraging knowledge gained from vast and diverse datasets.Our model is trained on a diverse dataset combining figshare, SARTAJ, and Br35H datasets, employing a federated learning approach for decentralized, privacy-preserving model training. The adoption of transfer learning further bolsters the model’s performance, making it adept at handling the intricate variations in MRI images associated with different types of brain tumors. The model demonstrates high precision (0.99 for glioma, 0.95 for meningioma, 1.00 for no tumor, and 0.98 for pituitary), recall, and F1-scores in classification, outperforming existing methods. The overall accuracy stands at 98%, showcasing the model’s efficacy in classifying various tumor types accurately, thus highlighting the transformative potential of federated learning and transfer learning in enhancing brain tumor classification using MRI images.

A review of natural products and small-molecule therapeutics acting on central nervous system malignancies: Approaches for drug development, targeting pathways, clinical trials, and challenges.

In 2021, the World Health Organization released the fifth edition of the central nervous system (CNS) tumor classification. This classification uses histopathology and molecular pathogenesis to group tumors into more biologically and molecularly defined entities. The prognosis of brain cancer, particularly malignant tumors, has remained poor worldwide, approximately 308,102 new cases of brain and other CNS tumors were diagnosed in the year 2020, with an estimated 251,329 deaths. The cost and time-consuming nature of studies to find new anticancer agents makes it necessary to have well-designed studies. In the present study, the pathways that can be targeted for drug development are discussed in detail. Some of the important cellular origins, signaling, and pathways involved in the efficacy of bioactive molecules againstCNS tumorigenesis or progression, as well as prognosis and common approaches for treatment of different types of brain tumors, are reviewed. Moreover, different study tools, including cell lines, in vitro, in vivo, and clinical trial challenges, are discussed. In addition, in this article, natural products as one of the most important sources for finding new chemotherapeutics were reviewed and over 700 reported molecules with efficacy against CNS cancer cells are gathered and classified according to their structure. Based on the clinical trials that have been registered, very few of these natural or semi-synthetic derivatives have been studied in humans. The review can help researchers understand the involved mechanisms and design new goal-oriented studies for drug development against CNS malignancies.

Transient deoxyhemoglobin formation as a contrast for perfusion MRI studies in patients with brain tumors: a feasibility study.

Background: Transient hypoxia-induced deoxyhemoglobin (dOHb) has recently been shown to represent a comparable contrast to gadolinium-based contrast agents for generating resting perfusion measures in healthy subjects. Here, we investigate the feasibility of translating this non-invasive approach to patients with brain tumors. Methods: A computer-controlled gas blender was used to induce transient precise isocapnic lung hypoxia and thereby transient arterial dOHb during echo-planar-imaging acquisition in a cohort of patients with different types of brain tumors (n = 9). We calculated relative cerebral blood volume (rCBV), cerebral blood flow (rCBF), and mean transit time (MTT) using a standard model-based analysis. The transient hypoxia induced-dOHb MRI perfusion maps were compared to available clinical DSC-MRI. Results: Transient hypoxia induced-dOHb based maps of resting perfusion displayed perfusion patterns consistent with underlying tumor histology and showed high spatial coherence to gadolinium-based DSC MR perfusion maps. Conclusion: Non-invasive transient hypoxia induced-dOHb was well-tolerated in patients with different types of brain tumors, and the generated rCBV, rCBF and MTT maps appear in good agreement with perfusion maps generated with gadolinium-based DSC MR perfusion.

Distance Analysis and Dimensionality Reduction using PCA on Brain Tumour MRI Scans

INTRODUCTION: Compression of MRI images while maintaining essential information, makes it easier to distinguish between different types of brain tumors. It also assesses the effect of PCA on picture representation modification and distance analysis between tumor classes.OBJECTIVES: The objective of this work is to enhance the interpretability and classification accuracy of highdimensional MRI scans of patients with brain tumors by utilising Principle Component Analysis (PCA) to reduce their complexity.METHODS:This study uses PCA to compress high-dimensional MRI scans of patients with brain tumors, focusing on improving classification using dimensionality reduction approaches and making the scans easier to understand.RESULTS: PCA efficiently reduced MRI data, enabling better discrimination between different types of brain tumors and significant changes in distance matrices, which emphasize structural changes in the data.CONCLUSION: PCA is crucial for improving the interpretability of MRI data.

Classification of Brain Tumors: A Comparative Approach of Shallow and Deep Neural Networks

Brain tumors can be generated anywhere in the brain, with an extensive size range and morphology that makes it challenging to identify and classify. Classifying brain tumors is essential for developing personalized treatment plans. Different types of brain tumors have different responses to treatment, and an accurate classification can help medical professionals develop treatment plans tailored to each patient’s needs. Therefore, this case study aimed to classify T1-weighted contrast-enhanced images of three types of tumors through various approaches, from shallow neural networks to fine-tuning deep neural networks trained. Comparing shallow and deep neural network approaches could help to understand the trade-offs between their performance, interoperability, interpretability, benefits, limitations, scopes, and overall, choosing the best method for a given problem.

Enhancing brain tumor detection: a novel CNN approach with advanced activation functions for accurate medical imaging analysis.

Brain tumors are characterized by abnormal cell growth within or around the brain, posing severe health risks often associated with high mortality rates. Various imaging techniques, including magnetic resonance imaging (MRI), are commonly employed to visualize the brain and identify malignant growths. Computer-aided diagnosis tools (CAD) utilizing Convolutional Neural Networks (CNNs) have proven effective in feature extraction and predictive analysis across diverse medical imaging modalities. This study explores a CNN trained and evaluated with nine activation functions, encompassing eight established ones from the literature and a modified version of the soft sign activation function. The latter demonstrates notable efficacy in discriminating between four types of brain tumors in MR images, achieving an accuracy of 97.6%. The sensitivity for glioma is 93.7%; for meningioma, it is 97.4%; for cases with no tumor, it is 98.8%; and for pituitary tumors, it reaches 100%. In this manuscript, we propose an advanced CNN architecture that integrates a newly developed activation function. Our extensive experimentation and analysis showcase the model's remarkable ability to precisely distinguish between different types of brain tumors within a substantial and diverse dataset. The findings from our study suggest that this model could serve as an invaluable supplementary tool for healthcare practitioners, including specialized medical professionals and resident physicians, in the accurate diagnosis of brain tumors.

CSIG-14. THE IMPACT OF CORTICOTROPHIN RELEASING HORMONE STRESS NEURO-HORMONES ON THE VIABILITY AND PROLIFERATION OF BRAIN TUMOURS

Abstract The majority of brain tumour sub-types are of glial origin. These include high-grade gliomas (HGGs), which lack effective targeted therapies and are thus invariably lethal. We have shown that stress leads to proliferation of brain glia. Such stress-induced glial cell division could provide opportunities for detrimental genetic mutations which increase the risk of oncogenesis. Identifying stress-induced glial plasticity could uncover molecular targets that directly influence glioma viability. Corticotropin-releasing hormone (CRH) stress neurohormones, including urocortin I-III (UCN I-III) are key mediators of stress signalling in the brain including neuroplastic processes. The aims of this project were to compare the expression CRH peptides and receptors in different types of brain tumours and their impact on cells viability and proliferation. Five different brain tumour cell lines were used (two biopsy-derived-in-house-established HGGs – UP007 and SEBTA023 – and three commercially available Medulloblastomas, MBs – UW402, CHLA-01-MED and CHLA-01R-MED). The gene expression profiles of the CRH family were accessed by qRT-PCR (n = 5) and the protein expression by Immunocytochemistry and flow cytometry (FC, n = 3). The effects of targeted drug modulation were assessed through viability (MTS, n = 3) and proliferation assays (Cyquant NF cell proliferation, n = 3). Statistical significance of qRT-PCRs and FC were calculated through a Kruskal-Wallis test, and for viability and proliferation assays through a two-way ANOVA approach. Values of P < 0.05 were accepted as significant. The qRT-PCRs revealed a higher expression of stress neuro-hormones mRNA in MBs compared to HGGs. Immunocytochemistry confirmed the protein expression of CRH and downstream receptors in all cell lines, while FC revealed a higher expression of CRH-Rs in HGGs than MBs. The pharmacological modulation of CRH-Rs induced contrasting effects on cellular viability and proliferation, suggesting patient-specific CRH-CRH-R pathway. This raises the prospect of repurposing available CRH-R ligands for targeted treatment of susceptible tumour types.

Edge impulse-based convolutional neural network for brain tumor classification

Brain tumor, a type of intracranial tumor, poses significant threats to human health. In an effort to improve the accuracy and efficiency of tumor recognition and classification, this paper used convolutional neural network to train the model through the Edge Impulse platform. In this paper, BrainChip's Keras-based classification model was selected as the learning block. The initial data set was broken down into training set and test set categories for data processing. Data in the training set made up 88% of the overall data set, whereas data in the test set made up 12% of the total data set. In this paper, the number of training epochs was set as 10. In addition, under the condition that other conditions remain unchanged, this study conducted the experiment with the learning rates of 0.0005, 0.001, 0.002 and 0.003 respectively. After training models with different learning rates through convolutional neural network, four groups of results with different accuracy were obtained in this study. Finally, this study's experiment produced the most accurate results (87.0%) when the learning rate was 0.002. The findings of this study suggest that training a CNN model can effectively and accurately identify different types of brain tumors in a large dataset.

The Effectiveness of OsiriX and the Anatomage Virtual Dissection Table in Enhancing Neuroanatomy and Neuroradiology Teaching.

In recent years with the advent of technology in medical education, teaching methodology has shifted towards heavy use of online-learning modalities. This has been especially the case for anatomy and radiology courses since they require students to visualize structures of the human body. Several studies indicated that Anatomage and OsiriX can be effective at enhancing students' learning experiences in anatomy and radiology. This aim of this study is to assess the effectiveness of online case-based learning modules in teaching medical students about the anatomy and radiology of different types of brain tumors. Two online case-based learning modules were designed using Anatomage Table® and OsiriX DICOM viewer®, consisting of a clinical case and CT and MRI images. We recruited 36 fourth-year medical students that completed two 10-question quizzes (one on glioblastoma multiforme and one on pituitary adenomas). Participants were randomly assigned to either a study group that completed both modules prior to completing the quizzes, or a control group that completed the quizzes without access to the modules. The performance of both groups was compared to assess the effectiveness of the modules. Participants in the study group also completed a feedback survey to assess the quality and convenience of using the modules. Students who used the case-based learning modules performed significantly better than those who did not (Quiz 1: mean = 6.56 vs 3.28, p<0.01. Quiz 2: mean = 6.67 vs 3.06, p<0.01). Students who completed the modules would like to see similar modules used in teaching anatomy and radiology in the future (64%). They found them easy to navigate (72%), useful in teaching anatomy and radiology (72%), and helpful in improving understanding of anatomical and radiological clinical correlations (77%). Online case-based learning modules created using Anatomage and OsiriX can be used effectively in teaching medical students about the anatomy and radiology of different types of brain tumors.

CNN-based Learning Approach for Detecting Brain Tumors from Magnetic Resonance Images (MRIs)

Abstract: The majority of malignancies that originate in the CNS occur in the brain. Annually, almost 11,700 people are told they have a brain tumour. About 34% of men and 36% of women survive 5 years after being diagnosed with a brain or central nervous system tumour. Benign tumours, malignant tumours, pituitary tumours, and so on are all different types of brain tumours. A patient's life expectancy can be increased via careful diagnosis, treatment, and planning. Magnetic resonance imaging (MRI) is the gold standard for diagnosing tumours in the brain. Scanning generates a massive amount of data in the form of images. A radiologist is the professional who looks at these scans. Manual examinations are unreliable because brain tumours are complex. ML and AI-based classification approaches outperform human-based methods. Thus, a global detection and classification system using deep learning techniques like CNN, ANN, and TL will help clinicians worldwide

Efficient simultaneous segmentation and classification of brain tumors from MRI scans using deep learning

Brain tumors can be difficult to diagnose, as they may have similar radiographic characteristics, and a thorough examination may take a considerable amount of time. To address these challenges, we propose an intelligent system for the automatic extraction and identification of brain tumors from 2D CE MRI images. Our approach comprises two stages. In the first stage, we use an encoder-decoder based U-net with residual network as the backbone to detect different types of brain tumors, including glioma, meningioma, and pituitary tumors. Our method achieved an accuracy of 99.60%, a sensitivity of 90.20%, a specificity of 99.80%, a dice similarity coefficient of 90.11%, and a precision of 90.50% for tumor extraction. In the second stage, we employ a YOLO2 (you only look once) based transfer learning approach to classify the extracted tumors, achieving a classification accuracy of 97%. Our proposed approach outperforms state-of-the-art methods found in the literature. The results demonstrate the potential of our method to aid in the diagnosis and treatment of brain tumors.

Develop Model for Detection and Localization of Brain Tumor

Abstract: A brain tumor is growth of damaged cells in your brain. There are many different types of brain tumors. Some brain tumors are non-cancerous (benign) and some brain tumors are cancerous (malignant). Brain tumors can start in your brain (primary brain tumors), or cancer can start in other parts of your body and spread to your brain as secondary (metastatic) brain tumors. The rate at which a brain tumor grows can vary widely. Brain tumor growth rate and location determine how your nervous system functions. One of the most important and useful tests performed on the brain is an MRI, which is magnetic resonance imaging. All images are tested and analyzed by a radiologist, due to the rapid development in neural networks and machine learning techniques of high performance systems for detecting the diseased part of a person. Machine learning and neural network models play an important role in computer vision. In this revive paper, we did an analysis on many object detection model and summarize the research done on this domain.

A Deep Convolutional Neural Network Framework for Enhancing Brain Tumor Diagnosis on MRI Scans

Brain tumors are a critical public health concern, often resulting in limited life expectancy for patients. Accurate diagnosis of brain tumors is crucial to develop effective treatment strategies and improve patients' quality of life. Computer-aided diagnosis (CAD) systems that accurately classify tumor images have been challenging to develop. Deep convolutional neural network (DCNN) models have shown significant potential for tumor detection, and outperform traditional deep neural network models. In this study, a novel framework based on two pre-trained deep convolutional architectures (VGG16 and EfficientNetB0) is proposed for classifying different types of brain tumors, including meningioma, glioma, and pituitary tumors. Features are extracted from MR images using each architecture and merged before feeding them into machine learning algorithms for tumor classification. The proposed approach achieves a training accuracy of 98% and a test accuracy of 99% on the brain-tumor-classification-mri dataset available on Kaggle and btc_navoneel. The model shows promise to improve the accuracy and generalizability of medical image classification for better clinical decision support, ultimately leading to improved patient outcomes.

Distinct gene expression patterns of SOX2 and SOX2OT variants in different types of brain tumours

Numerous investigations have been recently published on the dysregulated expression of long-noncoding RNAs (lncRNAs) in various cancer types, emphasizing that abnormal lncRNA expression is a major contributor to tumourigenesis. A broad spectrum of lncRNAs is expressed in the central nervous system, where these RNAs seem to play key roles in brain development and function. In addition to expressing SOX2, a master regulator of pluripotency that lies within its third intron, lncRNA SOX2OT has a proposed role in regulating neural development. Based on our previous studies, alternative splicing of SOX2OT generates two alternatively spliced variants (SOX2OT-S1 and SOX2OT-S2). The present study investigated the expression patterns of SOX2OT variants and SOX2 in three principal types of brain tumours (gliomas, meningiomas and pituitary adenomas) and in four brain tumour cell lines (U87-MG, 1321N1, A172 and DAOY). Total RNAwas extracted from 34 human brain tumour specimens, and the expression profile of target genes was measured using a real-time reverse transcription PCR approach. Our data revealed distinct expression patterns for SOX2OT variants and SOX2 in the brain tumour samples, indicating their potential involvement in brain tumourigenesis. Moreover, our results highlighted the potential usefulness of SOX2OT-S1, SOX2OT-S2, and SOX2 in molecular diagnosis and brain tumour classification.

Translating Single Cell Secrets to Cancer Evolution Accelerates Personalized Therapy

Translating Single Cell Secrets to Cancer Evolution Accelerates Personalized Therapy

A Novel Brain Tumor Classification Model Using Machine Learning Techniques

The objective of this research work is to classify brain tumor images into 4 different classes by using Convolutional Neural Network (CNN) algorithm i.e. a deep learning method with VGG16 architecture. The four classes are pituitary, glioma, meningioma, and no tumor. The dataset used for this research is a publicly available MRI Image dataset of brain tumor with 7023 images. The methodology followed in this project includes data pre-processing, model building, and evaluation. The dataset is pre-processed by resizing the images to 64x64 and normalizing the pixel values. The VGG16 architecture is used to build the CNN model, and it is trained on the pre-processed data for 10 epochs with a batch size of 64. The model is evaluated using the area under the operating characteristic curve (AUC) metric of the receiver. The results of this project show that the CNN model with VGG16 architecture achieves an AUC of 0.92 for classifying brain tumor images into four different classes. The model performs best for classifying meningioma with AUC of 0.90, followed by pituitary with AUC of 0.91, glioma with AUC of 0.93, and no tumor with AUC of 0.89. In conclusion, the CNN model with VGG16 architecture is an effective approach for classifying brain tumor images into multiple classes. The model achieves high accuracy in identifying different types of brain tumors, which could potentially aid in early diagnosis and treatment of brain tumors.

Precise Multi-Class Classification of Brain Tumor via Optimization Based Relevance Vector Machine

The objective of this research is to examine the use of feature selection and classification methods for distinguishing different types of brain tumors. The brain tumor is characterized by an anomalous proliferation of brain cells that can either be benign or malignant. Most tumors are misdiagnosed due to the variability and complexity of lesions, which reduces the survival rate in patients. Diagnosis of brain tumors via computer vision algorithms is a challenging task. Segmentation and classification of brain tumors are currently one of the most essential surgical and pharmaceutical procedures. Traditional brain tumor identification techniques require manual segmentation or handcrafted feature extraction that is error-prone and time-consuming. Hence the proposed research work is mainly focused on medical image processing, which takes Magnetic Resonance Imaging (MRI) images as input and performs preprocessing, segmentation, feature extraction, feature selection, similarity measurement, and classification steps for identifying brain tumors. Initially, the median filter is practically applied to the input image to reduce the noise. The graph-cut segmentation technique is used to segment the tumor region. The texture feature is extracted from the output of the segmented image. The extracted feature is selected by using the Ant Colony Optimization (ACO) algorithm to improve the performance of the classifier. This probabilistic approach is used to solve computing issues. The Euclidean distance is used to calculate the degree of similarity for each extracted feature. The selected feature value is given to the Relevance Vector Machine (RVM) which is a multi-class classification technique. Finally, the tumor is classified as abnormal or normal. The experimental result reveals that the proposed RVM technique gives a better accuracy range of 98.87% when compared to the traditional Support Vector Machine (SVM) technique.

Establishing Brain Tumor Stem Cell Culture from Patient Brain Tumors and Imaging Analysis of Patient-Derived Xenografts.

Brain tumor stem cells (BTSCs) have been isolated from different types of brain tumors including glioblastoma. Although BTSCs share common characteristics with neural stem cells (NSCs), such as capacity to self-renew and undergo long-term proliferation, they have tumor-propagating capabilities. A small population of BTSC can give rise to secondary tumor when transplanted into severe immunodeficient (SCID) mice. The histological and cytological features, as well as genetic heterogeneity of the xenografted tumors in mice, closely resemble those of primary tumors in patients. Patient-derived xenografts (PDX), therefore, provide a clinically relevant model to study brain tumors. Here, we describe our protocol for establishing BTSC cultures following surgical excision of human brain tumors and the procedures to conduct PDX studies in SCID mice. We also provide our detailed step-by-step protocol on in vivo imaging system (IVIS) of the PDX tumors as a noninvasive method to trace the cells and tumor volume.