Brain Tumor Identification Research Articles

Probabilistic hierarchical clustering based identification and segmentation of brain tumors in magnetic resonance imaging.

The automatic segmentation of the abnormality region from the head MRI is a challenging task in the medical science domain. The abnormality in the form of the tumor comprises the uncontrolled growth of the cells. The automatic identification of the affected cells using computerized software systems is demanding in the past several years to provide a second opinion to radiologists. In this paper, a new clustering approach is introduced based on the machine learning aspect that clusters the tumor region from the input MRI using disjoint tree generation followed by tree merging. Further, the proposed algorithm is improved by introducing the theory of joint probabilities and nearest neighbors. Later, the proposed algorithm is automated to find the number of clusters required with its nearest neighbors to do semantic segmentation of the tumor cells. The proposed algorithm provides good semantic segmentation results having the DB index-0.11 and Dunn index-13.18 on the SMS dataset. While the experimentation with BRATS 2015 dataset yields Dice complete=80.5 %, Dice core=73.2 %, and Dice enhanced=62.8 %. The comparative analysis of the proposed approach with benchmark models and algorithms proves the model's significance and its applicability to do semantic segmentation of the tumor cells with the average increment in the accuracy of around ±2.5 % with machine learning algorithms.

Blood-based biomarkers: diagnostic value in brain tumors (focus on gliomas).

Brain tumors, especially gliomas, are known for high lethality. It is currently understood that the correlations of tumors with coagulation and inflammation have been gradually revealed. This study aimed to explore the potential value of several reported peripheral blood parameters as comprehensively as possible, with preoperative diagnosis and identification of brain tumors (focus on gliomas). Patients with central nervous system tumors (craniopharyngioma, ependymoma, spinal meningioma, acoustic neuroma, brain metastases, meningioma, and glioma) or primary trigeminal neuralgia admitted to our hospital were retrospectively analyzed. The results of the routine coagulation factor test, serum albumin test, and blood cell test in peripheral blood were recorded for each group of patients on admission. Neutrophil-lymphocyte ratio (NLR), derived NLR (dNLR), platelet-lymphocyte ratio (PLR), lymphocyte-monocyte ratio (LMR), prognostic nutritional index (PNI), the systemic immune-inflammation index (SII), pan-immune-inflammation value (PIV), and their pairings were calculated. Their ability to identify brain tumors and their correlation with glioma grade were analyzed. A total of 698 patients were included in this retrospective case-control study. Glioma patients had higher NLR, SII, and PIV but lower LMR. The NLR in the brain metastasis group was lower than that in the control, meningioma, and acoustic neuroma groups, but the SII and PIV were higher than those in the ependymoma group. Fibrinogen, white blood cell count, neutrophil count, NLR, SII, and PIV in the GBM group were higher than those in the control group. In all comparisons, NLR and NLR + dNLR showed the greatest accuracy, with areas under the curve (AUCs) of 0.7490 (0.6482-0.8498) and 0.7481 (0.6457-0.8505), respectively. PIV, dNLR + PIV, and LMR + PIV ranked second, with AUCs of 0.7200 (0.6551-0.7849), 0.7200 (0.6526-0.7874), 0.7204 (0.6530-0.7878) and 0.7206 (0.6536-0.7875), respectively. NLR, PIV, and their combinations show high sensitivity and specificity in the diagnosis of brain tumors, especially gliomas. Overall, our results provide evidence for these convenient and reliable peripheral blood markers.

Diagnosis of Brain Tumor Using Deep Learning Stacked Classifier -Ml Algorithms

Brain tumours are very lethal, and radiologists have a difficult time classifying them due to the varied characteristics of tumour cells. Recently, computer-aided diagnostic (CAD) systems have shown promise as an enabling tool for detecting brain tumours using MRI. The bottom layers of natural photos and medical images are distinct, although both are used in modern applications of pre-trained models. This research presents a strategy for early identification of brain tumours that involves the extraction and concatenation of many characteristics at different levels. These results are credible since VGG-16 is a pretrained deep learning model. These two models were used to compare two scenarios for identifying and categorising brain tumours. To begin classifying brain tumours, characteristics were first retrieved from various VGG modules using the pretrained VGG_16 model. Second, we used PCA, LDA, and ICA to derive features. These merged characteristics were then fed into a hybrid classifier to determine the kind of brain tumour. Finally, the presence of brain tumour is be identified.The results obtained using matlab tool are compared in terms of various parameters evaluated.

Automated Identification and Localization of Brain Tumor in MRI Using U-Net Segmentation and CNN-LSTM Classification

Nowadays, the use of computers to evaluate medical images automatically is critical part of the life. Today's treatment method relies heavily on early diagnosis and accurate disease identification, which were formerly difficult for medical research to achieve. Brain Magnetic Resonance Imaging (MRI) is essential to the detection and treatment of brain tumor (BT). Tumor of the brain are the result of brain cell division that has gone awry or is otherwise out of control. The manual MRI segmentation of BT is a difficult and time-consuming process. The most critical factor in the effective treatment and identification of BT is the ability to accurately locate the tumor. The detection of BT is regarded as a difficult task in medical image processing. For analysing and interpreting MRI, there are semi-automatic and fully automated systems that require large-scale professional input and evaluation, with varying degrees of effectiveness. Automated identification and extraction of the tumor's localization from brain MRI will be proposed in this paper. To achieve this goal, the data collected from Kaggle and the collected data are processed. Then the U-Net is employed to segment the tumor region from the MRI. Next, the MRI is classified using DL models like Convolutional Neural Network (CNN), and the hybrid Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM). Both process segmentation and classification are evaluated using the metrics. From the evaluation, it is identified that CNN-LSTM outperforms the CNN model.

A Review of Current Developments in The Detection of Brain Cancer Using Machine Learning: A Systematic Analysis of Methodology, Comparisons, Results and Challenges

Among the deadliest illnesses that is not initially recognized in the context of cancer is brain cancer. Cancerous cells are created when cells proliferate quickly and uncontrollably. The changes in tumour location, size, and shape severely impair the ability to detect brain cancers. The wrong diagnosis of a brain tumour can have terrible and fatal consequences. So there is need of high level accuracy to find the tumor classification. The categorization, segmentation, analysis, and detection of brain cancer are the main topics of this work. In order to aid researchers, This study intends to offer an extensive exploration of the existing literature regarding brain tumor identification through the utilization of magnetic resonance imaging. Three different types of medical imaging for brain cancer were subjected to machine learning approaches (feature extraction, augmentation methods, segmentation, and anatomy of brain tumours) and identifies current issues that must be resolved for various machine learning algorithms to be applied widely in the treatment of personalised brain cancer. Ultimately, this comprehensive review compiles relevant scholarly works concerning brain tumor detection, accentuating its advantages, limitations, progressions, and challenges, thus paving the way for potential forthcoming investigations.

TumorDetNet: A unified deep learning model for brain tumor detection and classification.

Accurate diagnosis of the brain tumor type at an earlier stage is crucial for the treatment process and helps to save the lives of a large number of people worldwide. Because they are non-invasive and spare patients from having an unpleasant biopsy, magnetic resonance imaging (MRI) scans are frequently employed to identify tumors. The manual identification of tumors is difficult and requires considerable time due to the large number of three-dimensional images that an MRI scan of one patient's brain produces from various angles. Moreover, the variations in location, size, and shape of the brain tumor also make it challenging to detect and classify different types of tumors. Thus, computer-aided diagnostics (CAD) systems have been proposed for the detection of brain tumors. In this paper, we proposed a novel unified end-to-end deep learning model named TumorDetNet for brain tumor detection and classification. Our TumorDetNet framework employs 48 convolution layers with leaky ReLU (LReLU) and ReLU activation functions to compute the most distinctive deep feature maps. Moreover, average pooling and a dropout layer are also used to learn distinctive patterns and reduce overfitting. Finally, one fully connected and a softmax layer are employed to detect and classify the brain tumor into multiple types. We assessed the performance of our method on six standard Kaggle brain tumor MRI datasets for brain tumor detection and classification into (malignant and benign), and (glioma, pituitary, and meningioma). Our model successfully identified brain tumors with remarkable accuracy of 99.83%, classified benign and malignant brain tumors with an ideal accuracy of 100%, and meningiomas, pituitary, and gliomas tumors with an accuracy of 99.27%. These outcomes demonstrate the potency of the suggested methodology for the reliable identification and categorization of brain tumors.

Enhancing Brain Tumor Detection on MRI Images Using an Innovative VGG-19 Model-Based Approach

Early detection and diagnosis of brain tumors have a critical impact on the treatment of brain tumor patients. This is because initiating interventions early directly impacts the patient's chances of continuing their life. In the field of medical research, various methods are employed for the detection of brain tumors. Among these methods, magnetic resonance imaging (MRI) is the most popular due to its superior image quality. By leveraging technological advancements, the utilization of deep learning techniques in the identification of brain tumors ensures both high accuracy and simplification of the process. In a conducted study, a new model was developed by utilizing the VGG-19 architecture, a popular convolutional neural network model, to achieve high accuracy in brain tumor detection. In the study, precision, F1 score, accuracy, specificity, Matthews correlation coefficient, and recall metrics were used to evaluate the performance of the developed model. The deep learning model developed for brain tumor detection was trained and evaluated on an open-source dataset consisting of MRI images of gliomas, meningiomas, pituitary tumors, and healthy brains. The results obtained from the study demonstrate the promising potential of using the developed model in clinical applications for brain tumor detection. The high accuracy achieved by the developed model emphasizes its potential as an auxiliary resource for healthcare professionals in brain tumor detection. This research aims to evaluate the model as a valuable tool that can assist physicians in making informed treatment decisions regarding brain tumor diagnosis.

Brain Tumor Classification from MRI Using Image Enhancement and Convolutional Neural Network Techniques.

The independent detection and classification of brain malignancies using magnetic resonance imaging (MRI) can present challenges and the potential for error due to the intricate nature and time-consuming process involved. The complexity of the brain tumor identification process primarily stems from the need for a comprehensive evaluation spanning multiple modules. The advancement of deep learning (DL) has facilitated the emergence of automated medical image processing and diagnostics solutions, thereby offering a potential resolution to this issue. Convolutional neural networks (CNNs) represent a prominent methodology in visual learning and image categorization. The present study introduces a novel methodology integrating image enhancement techniques, specifically, Gaussian-blur-based sharpening and Adaptive Histogram Equalization using CLAHE, with the proposed model. This approach aims to effectively classify different categories of brain tumors, including glioma, meningioma, and pituitary tumor, as well as cases without tumors. The algorithm underwent comprehensive testing using benchmarked data from the published literature, and the results were compared with pre-trained models, including VGG16, ResNet50, VGG19, InceptionV3, and MobileNetV2. The experimental findings of the proposed method demonstrated a noteworthy classification accuracy of 97.84%, a precision success rate of 97.85%, a recall rate of 97.85%, and an F1-score of 97.90%. The results presented in this study showcase the exceptional accuracy of the proposed methodology in accurately classifying the most commonly occurring brain tumor types. The technique exhibited commendable generalization properties, rendering it a valuable asset in medicine for aiding physicians in making precise and proficient brain diagnoses.

Brain tumor detection model based on CNN and threshold segmentation

Brain tumours pose a substantial global health issue, emphasising the criticality of timely and precise identification to ensure optimal treatment outcomes. This research introduces an innovative methodology for the identification of brain tumours by employing a fusion of Convolutional Neural Networks (CNNs) with threshold segmentation techniques. The objective of the suggested model is to improve the precision and effectiveness of brain tumour identification in medical imaging, namely Magnetic Resonance Imaging (MRI) scans. Fast and precise diagnosis is necessary in the medical profession for effective treatment, but current technologies lack this capability. For successful therapy, it is therefore necessary to develop an effective diagnosis application. Global threshold segmentation for pre-processing is used in this study. Image capture and de-noising were completed in the first stage, while classification and regression were completed in the second stage using ML approaches. A computer-aided automated identification method is the computational technique used in this study. This investigation uses one hundred twenty (120) brain scans from a real-time MRI brain database, of them 15 normal and 105 abnormal. According to performance metrics, the accuracy of training and testing pictures was 99.46%. Comparing this method to recently published methods, it is determined that LR-ML with the threshold segmentation has a rapid, precise brain diagnostic system.

Enhancing brain tumor classification with transfer learning: Leveraging DenseNet121 for accurate and efficient detection

AbstractBrain tumors pose a serious neurological threat to human life, necessitating improved detection and classification methods. Deep transfer learning (TL), in particular in key tumor categories such as meningioma, pituitary, glioma, and instances without tumors, has shown to be a new and successful method for tumor identification and classification. In this work, the efficacy of two pre‐trained TL methods—Inceptionv3 and DenseNet121—was examined for correctly classifying certain kinds of brain tumors. The experimental findings show that the DenseNet‐121 model, using the TL approach, performed better than other models in terms of accuracy for the identification and classification of brain tumors. The classification test results were impressive, with DenseNet‐121 reaching an astounding 99.95% accuracy and precision, recall, and F1‐measure scores of 97.7%, 92.1%, and 94.8%, respectively. DenseNet‐121 demonstrated 100% and 92.42% training and validation accuracies, respectively, highlighting its potential as an effective and precise diagnosis tool for brain malignancies.

Improving Alzheimer’s Disease and Brain Tumor Detection Using Deep Learning with Particle Swarm Optimization

Convolutional Neural Networks (CNNs) have exhibited remarkable potential in effectively tackling the intricate task of classifying MRI images, specifically in Alzheimer’s disease detection and brain tumor identification. While CNNs optimize their parameters automatically through training processes, finding the optimal values for these parameters can still be a challenging task due to the complexity of the search space and the potential for suboptimal results. Consequently, researchers often encounter difficulties determining the ideal parameter settings for CNNs. This challenge necessitates using trial-and-error methods or expert judgment, as the search for the best combination of parameters involves exploring a vast space of possibilities. Despite the automatic optimization during training, the process does not guarantee finding the globally-optimal parameter values. Hence, researchers often rely on iterative experimentation and expert knowledge to fine-tune these parameters and maximize CNN performance. This poses a significant obstacle in developing real-world applications that leverage CNNs for MRI image analysis. This paper presents a new hybrid model that combines the Particle Swarm Optimization (PSO) algorithm with CNNs to enhance detection and classification capabilities. Our method utilizes the PSO algorithm to determine the optimal configuration of CNN hyper-parameters. Subsequently, these optimized parameters are applied to the CNN architectures for classification. As a result, our hybrid model exhibits improved prediction accuracy for brain diseases while reducing the loss of function value. To evaluate the performance of our proposed model, we conducted experiments using three benchmark datasets. Two datasets were utilized for Alzheimer’s disease: the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and an international dataset from Kaggle. The third dataset focused on brain tumors. The experimental assessment demonstrated the superiority of our proposed model, achieving unprecedented accuracy rates of 98.50%, 98.83%, and 97.12% for the datasets mentioned earlier, respectively.

An optimized XGBoost technique for accurate brain tumor detection using feature selection and image segmentation

An abnormal multiplication of cells in the brain forms malignant and benign brain tumors. Malignant brain tumors are more prevalent than benign ones. Detecting a tumor’s physical features may be tedious and time-consuming for medical experts due to the complexity of a tumor’s structure and noise growth in Magnetic Resonance Imaging (MRI) data. Detecting and localizing tumors at an early stage in their growth is crucial. Complex structures and precise clinical identification using segmentation are considered the norm in medical imaging. As the quantity of images rises, the radiologist’s manual tumor assessment might result in an incorrect conclusion. Due to the likelihood of human error, the evaluation and categorization of medical images require an automated method. This paper optimizes the eXtreme Gradient Boosting (XGBoost) approach through image processing and feature selection for the reliable identification of brain tumors. Images are improved using the Contrast-Limited Adaptive Histogram Equalization (CLAHE) method. The K-Means algorithm divides images into sections. This segmentation assists in pinpointing the specific area of interest. Particle Swarm Optimization (PSO) is used to choose characteristics. The data is classified by XGBoost, Naive Bayes, and the Iterative Dichotomiser 3 (ID3). According to the experimental results, the suggested PSO-XGBoost model achieved 97% accuracy, 97% specificity, 98% precision, and 98% recall.

Brain Tumor Identification Based on AlexNet and VGG

Early diagnosis of brain tumor symptoms through Computer Tomography (CT) imaging is an important means of treating brain tumors. However, doctors who identify and diagnose brain tumors with naked eyes may cause misdiagnosis due to overwork or inexperience, which greatly affects the rehabilitation of brain tumor patients. In recent years, there is a lack of a clinical application tool for automatic identification and classification of brain tumor CT images to help doctors improve the rate of brain tumor treatment. This study proposes an approach for identifying and classifying brain tumors using a modified AlexNet and Visual Geometry Group (VGG). The methodology involves the comparative analysis of the performance of the modified AlexNet and VGG in identifying and classifying brain tumors. Our findings reveal that the modified AlexNet demonstrates superior performance, with an accuracy rate of 69.54%, in comparison to the VGG. The proposed approach holds tremendous potential for future clinical application as an auxiliary tool to aid medical professionals in accurately diagnosing brain tumors.

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.

Brain Tumor Diagnosis Using CNN and Image Processing

Abstract: Among the most tedious and time-consuming tasks in medical image processing is brain tumor identification and segmentation. Magnetic resonance imaging, or MRI, is a medical procedure that radiologists primarily utilize to examine the human body without having to do surgery. The substantial information that MRI provides on human soft tissue aids in the diagnosis of brain tumors. It is crucial to accurately segment MRI images in order to detect a brain tumour utilizing computeraided clinical techniques. Brain tumor identification facilitates determining the precise size and location of the tumor. Initially, the scanned image's quality is upgraded, and tumors are located in the image using morphological operators

Deep Learning Unveils Hidden Insights: Advancing Brain Tumor Diagnosis

Timely detection and treatment are crucial in managing brain tumors, a severe medical condition. MRI is a commonly used diagnostic tool to detect brain tumors. However, because of the complex structure of the brain and the wide range of tumors sizes and forms, MRI scan interpretation can be time-consuming and error-prone. The automated detection and segmentation of brain tumors has shown encouraging results with to recent developments in DL techniques. We suggest a CNN-RNNs and GANs based DL technique for brain tumor identification in this paper. Transfer learning and data augmentation techniques are used in the suggested method to train the CNN on a sizable dataset of MRI images labelled with tumor areas. The suggested strategy, according to experimental findings, is more accurate than the most advanced techniques now available for finding brain tumors. The suggested strategy has the potential to help radiologists identify brain tumors quickly and reliably, improving patient outcomes.

Identification of incidental brain tumors in prostate cancer patients via PSMA PET/CT.

Brain metastases are rare in patients with prostate cancer and portend poor outcome. Prostate-specific membrane antigen positron emission tomography (PSMA PET)/CT scans including the brain have identified incidental tumors. We sought to identify the incidental brain tumor detection rate of PSMA PET/CT performed at initial diagnosis or in the setting of biochemical recurrence. An institutional database was queried for patients who underwent 68Ga-PSMA-11 or 18F-DCFPyL (18F-piflufolastat) PET/CT imaging at an NCI-designated Comprehensive Cancer Center from 1/2018 to 12/2022. Imaging reports and clinical courses were reviewed to identify brain lesions and describe clinical and pathologic features. Two-thousand seven hundred and sixty-three patients underwent 3363 PSMA PET/CT scans in the absence of neurologic symptoms. Forty-four brain lesions were identified, including 33 PSMA-avid lesions: 10 intraparenchymal metastases (30%), 4 dural-based metastases (12%), 16 meningiomas (48%), 2 pituitary macroadenomas (6%), and 1 epidermal inclusion cyst (3%) (incidences of 0.36, 0.14, 0.58, 0.07, and 0.04%). The mean parenchymal metastasis diameter and mean SUVmax were 1.99cm (95%CI:1.25-2.73) and 4.49 (95%CI:2.41-6.57), respectively. At the time of parenchymal brain metastasis detection, 57% of patients had no concurrent extracranial disease, 14% had localized prostate disease only, and 29% had extracranial metastases. Seven of 8 patients with parenchymal brain metastases remain alive at a median 8.8 months follow-up. Prostate cancer brain metastases are rare, especially in the absence of widespread metastatic disease. Nevertheless, incidentally detected brain foci of PSMA uptake may represent previously unknown prostate cancer metastases, even in small lesions and in the absence of systemic disease.

Smart Brain Tumor Prediction System

Brain is the regulatory unit in human body. It controls the functions such as memory, vision, hearing, knowledge, personality, problem solving, etc. The main reason for brain tumor is the abandoned progress of brain cells. Many health organizations have recognized brain tumor as the second foremost dispute that causes many human deaths all around the world. Identification of brain tumor at a premature stage offers opportunity of effective medical treatment. Use of Magnetic Resonance Imaging images have been recognized as more detailed and more consistent images when compared to Computed Tomography images. In this project we are predicting the brain tumor using machine learning algorithms. there are various techniques to detect brain tumor or neoplasms. The most competent and effective algorithm that we will be using in this project is CNN (Convolutional Neural Network).It is a Deep Learning Algorithm that is Used for Image Processing Starting with Pre-processing brain images then, segmenting them, feature extraction of unwanted noisy and blurry part, clustering and detection of the tumor are the methodologies of our Project. Key Word: Brain Tumor Prediction, MRI Images, CNN, Image Processing and Deep Learning.

Efficient MRI Segmentation and Detection of Brain Tumour using CNN

Image/Object detection is essential in numerous industries, such as medical imaging, aerial surveillance, the best manipulation and analysis, surgical microscopes, etc. This system's objective is to provide a benchmark for the identification and classification of brain tumours, specifically to determine using the SVM algorithm whether a tumour is cancerous or not. ANNs that apply empirical risk minimization are already widely employed to detect things. We are using the Support Vector Machine method to classify the images, which depends on structural risk minimization. Tumour extraction from medical images is performed using the SVM technique, and the tumour classification function is implemented using a Python-based system. The training dataset was used to test CNN approaches

Blockchain-enabled healthcare monitoring system for early Monkeypox detection.

The recent emergence of monkeypox poses a life-threatening challenge to humans and has become one of the global health concerns after COVID-19. Currently, machine learning-based smart healthcare monitoring systems have demonstrated significant potential in image-based diagnosis including brain tumor identification and lung cancer diagnosis. In a similar fashion, the applications of machine learning can be utilized for the early identification of monkeypox cases. However, sharing critical health information with various actors such as patients, doctors, and other healthcare professionals in a secure manner remains a research challenge. Motivated by this fact, our paper presents a blockchain-enabled conceptual framework for the early detection and classification of monkeypox using transfer learning. The proposed framework is experimentally demonstrated in Python 3.9 using a monkeypox dataset of 1905 images obtained from the GitHub repository. To validate the effectiveness of the proposed model, various performance estimators, namely accuracy, recall, precision, and F1-score, are employed. The performance of different transfer learning models, namely Xception, VGG19, and VGG16, is compared against the presented methodology. Based on the comparison, it is evident that the proposed methodology effectively detects and classifies the monkeypox disease with a classification accuracy of 98.80%. In future, multiple skin diseases such as measles and chickenpox can be diagnosed using the proposed model on the skin lesion datasets.