Brain Tumor Identification Research Articles

Enhanced Brain Tumor Detection and Privacy Preserving Using Federated Learning

Brain cancers pose significant difficulties for both diagnosis and treatment, underscoring the necessity for precise and private-protecting detection techniques. Federated learning is used to solve this, allowing several healthcare facilities to work together to train detection models without jeopardizing patient privacy. This paper presents an approach called federated learning that may be used to improve brain tumor identification while protecting patient privacy. Brain tumors are dangerous medical disorders that need to be accurately diagnosed in order to be effectively treated. However, sharing private patient information is a common practice in traditional medical data analysis methodologies, which raises privacy issues. Federated learning helps with this by enabling cooperative training of a common model amongst several hospitals or institutions without requiring the exchange of raw data. This method protects patient privacy by having each institution train the model using its own local data and only sharing model updates. We show through trials that our method is efficient in reliably identifying brain tumors while upholding privacy norms, presenting a viable option for improving medical diagnosis without jeopardizing patient privacy.

Enhanced Deep Learning Methodology for Detection and Identification of Brain Tumor using CNN

Enhanced Deep Learning Methodology for Detection and Identification of Brain Tumor using CNN

Detection and classification on MRI images of brain tumor using YOLO NAS deep learning model

If a brain tumor is not properly diagnosed, it might result in fatal consequences and major health issues. As a result, a key component of diagnosis is the early identification of brain tumors and the precise categorization of brain tumor types. This research work focusses on detection and classification such as pituitary, meningioma, glioma, and non-tumorous tumors from brain tumor MRI image using YOLO NAS model. Advances in deep learning have led to an increasing awareness of computer-aided diagnosis technology. To improve classification performance, the input data set is acquired from the REMBRANDT repository using the Digital Database of Brain Tumor Magnetic Resonance Images. The primary phases of this task include segmentation, classification, and pre-processing. The researcher preprocessed the RGB image to exclude any undesired spots before locating the ROI. For each brain tumor image, we used the hybrid anisotropic diffusion filtering (HADF) technique to remove noise. The Encoder-Decoder Network (En–DeNet), which uses a deep neural network based on U-Net as the encoder and a pre-trained EfficientNet as the decoder, is then used to segment the MRI images. A proposed model was developed using a deep learning-based YOLO NAS technique to identify brain cancers from MRI images such as meningioma tumors, non-tumor, glioma tumors, and pituitary tumors. The suggested model's classification performance is weighed based on parameters such as precision, F1-score, sensitivity, accuracy, and specificity. The proposed fusion method YOLO NAS has improved classification results of accuracy (AC = 0.997%), specificity (SP = 0.985%), precision (PR = 0.982%), F1-score (F1 = 0.992%), and sensitivity (SE = 0.985%) using 2570 MRI data for training and 630 data for testing and validation of brain tumor cases. The results show that the brain tumor type classification system based on the proposed YOLO NAS technique works better than the DNN, PDCNN, DenseNet-161, and DCNN-SGD model classifiers.

Enhanced MRI brain tumor detection and classification via topological data analysis and low-rank tensor decomposition

The advent of artificial intelligence in medical imaging has paved the way for significant advancements in the diagnosis of brain tumors. This study presents a novel ensemble approach that uses magnetic resonance imaging (MRI) to identify and categorize common brain cancers, such as pituitary, meningioma, and glioma. The proposed workflow is composed of a two-fold approach: firstly, it employs non-trivial image enhancement techniques in data preprocessing, low-rank Tucker decomposition for dimensionality reduction, and machine learning (ML) classifiers to detect and predict the type of brain tumor. Secondly, persistent homology (PH), a topological data analysis (TDA) technique, is exploited to extract potential critical areas in MRI scans. When paired with the ML classifier output, this additional information can help domain experts to identify areas of interest that might contain tumor signatures, improving the interpretability of ML predictions. When compared to automated diagnoses, this transparency adds another level of confidence and is essential for clinical acceptance. The performance of the system was quantitatively evaluated on a well-known MRI dataset, with an overall classification accuracy of 97.28% using an extremely randomized trees model. The promising results show that the integration of TDA, ML, and low-rank approximation methods is a successful approach for brain tumor identification and categorization, providing a solid foundation for further study and clinical application.

Tumor Detection in MRI Data using Deep Learning Techniques for Image Classification and Semantic Segmentation

This study aims to apply image classification and semantic segmentation methods using deep learning for accurate and efficient tumor detection in brain MRI data. It looks at ways to get over issues including interpretability of the model and a lack of annotated data. A pre-trained VGG19 model and a basic convolutional neural network (CNN) were trained using a public dataset of 273 brain MRI images for the purpose of image classification. Techniques for regularization, transfer learning, and data augmentation were used. A U-Net architecture was trained using manually created masks from the same dataset for semantic segmentation, with several training setups investigated. With a mean test accuracy of 77.19%, the pre-trained VGG19 model surpassed the basic CNN in picture categorization. The U-Net model performed better for semantic segmentation after training on the entire dataset for two epochs as opposed to training on a subset for ten epochs. The work acknowledges issues including data scarcity and the requirement for model interpretability and scalability, which call for additional research, but also underlines the potential of deep learning for brain tumor identification.

Enhancing brain tumor classification through ensemble attention mechanism.

Brain tumors pose a serious threat to public health, impacting thousands of individuals directly or indirectly worldwide. Timely and accurate detection of these tumors is crucial for effective treatment and enhancing the quality of patients' lives. The widely used brain imaging technique is magnetic resonance imaging, the precise identification of brain tumors in MRI images is challenging due to the diverse anatomical structures. This paper introduces an innovative approach known as the ensemble attention mechanism to address this challenge. Initially, the approach uses two networks to extract intermediate- and final-level feature maps from MobileNetV3 and EfficientNetB7. This assists in gathering the relevant feature maps from the different models at different levels. Then, the technique incorporates a co-attention mechanism into the intermediate and final feature map levels on both networks and ensembles them. This directs attention to certain regions to extract global-level features at different levels. Ensemble of attentive feature maps enabling the precise detection of various feature patterns within brain tumor images at both model, local, and global levels. This leads to an improvement in the classification process. The proposed system was evaluated on the Figshare dataset and achieved an accuracy of 98.94%, and 98.48% for the BraTS 2019 dataset which is superior to other methods. Thus, it is robust and suitable for brain tumor detection in healthcare systems.

Segmentation Synergy with a Dual U-Net and Federated Learning with CNNRF Models for Enhanced Brain Tumor Analysis

Background: Brain tumours represent a diagnostic challenge, especially in the imaging area, where the differentiation of normal and pathologic tissues should be precise. The use of up-to-date machine learning techniques would be of great help in terms of brain tumor identification accuracy from MRI data. Objective: This research paper aims to check the efficiency of a federated learning method that joins two classifiers, such as convolutional neural networks (CNNs) and random forests (R.F.F.), with dual U-Net segmentation for federated learning. This procedure benefits the image identification task on preprocessed MRI scan pictures that have already been categorized. Methods: In addition to using a variety of datasets, federated learning was utilized to train the CNN-RF model while taking data privacy into account. The processed MRI images with Median, Gaussian, and Wiener filters are used to filter out the noise level and make the feature extraction process easy and efficient. The surgical part used a dual U-Net layout, and the performance assessment was based on precision, recall, F1-score, and accuracy. Results: The model achieved excellent classification performance on local datasets as CRPs were high, from 91.28% to 95.52% for macro, micro, and weighted averages. Throughout the process of federated averaging, the collective model outperformed by reaching 97% accuracy compared to those of 99%, which were subjected to different clients. The correctness of how data is used helps the federated averaging method convert individual model insights into a consistent global model while keeping all personal data private. Conclusion: The combined structure of the federated learning framework, CNN-RF hybrid model, and dual U-Net segmentation is a robust and privacypreserving approach for identifying MRI images from brain tumors. The results of the present study exhibited that the technique is promising in improving the quality of brain tumor categorization and provides a pathway for practical utilization in clinical settings.

A Linear time shrinking-SL(t)-ViT approach for brain tumor identification and categorization

Automated brain tumor detection and classification systems have gained popularity in recent years because traditional diagnosis procedures are time-consuming and costly in nature. Deep learning(DL) methods, specifically pre-trained convolutional neural networks (CNNs), have shown promising results in accurately and rapidly classifying brain tumors. However, the lack of diverse magnetic resonance imaging (MRI) datasets has hindered the ability of DL algorithms to generalize effectively. To address this issue, this paper proposes a DL model using generative adversarial networks (GANs) in conjunction with data augmentation strategies and a structural similarity loss function employed for generating annotated images. A novel DL model inspired from Vision Transformer, Shrinking Linear Time Vision Transformer (SL(t)-ViT) network model is proposed for disease data classification. The model underwent extensive evaluation across multiple datasets, employing standard performance metrics to assess its efficacy in brain tumor identification. The proposed model achieved remarkable testing accuracies of 0.995, 0.996, 0.9954, 0.998, and 0.997 for binary classification tasks across multiple datasets, and 0.986, 0.982, 0.985, and 0.993 for multi-class classification tasks. These results underscore the superior performance of our proposed model, showcasing its capability to outperform state-of-the-art techniques. Specifically, it demonstrated a substantial margin of improvement, ranging from 1-2% for binary classification and 9-10% for multi-class classification, solidifying its position as a leading approach in brain tumor identification. Results demonstrate the efficacy of the proposed model, outperforming other models. Overall, this study highlights the potential of SL(t)-ViT and GANs in improving the accuracy, resource consumption, and efficiency of brain tumor diagnosis.

Prompt Multi-level Segmentation with Denoising Model with Fragile Correlated Feature Subset for Brain Tumor Classification.

Background Classifying brain tumors with extraordinary precision using images is critical for prognosis and treatment planning. The aberrant proliferation of brain cells characterizes brain tumors. Variations in neuronal development may occur among individuals. The classification of tumors as benign or malignant is contingent upon their rate of growth. A benign tumor remains localized at its site of origin; one that has spread to distant sites is malignant. Brain tumor identification may be difficult due to the unique characteristics of brain tumor cells. Objective This study presents a method that methodically improves the identification of brain tumor cells and the analysis of functional structures through the utilization of sample training that incorporates features extracted from Magnetic Resonance Imaging (MRI) images. In the image enhancement phase, the color information of the MRI image is converted to greyscale, and its margins are sharpened to facilitate the detection of finer details. For specialists or general practitioners to accurately diagnose life-threatening conditions, such as brain tumors, medical images are required. Picture denoising has been identified in recent research as a potentially fruitful area of study. It is critical to perform image cleanup while preserving the sharpness of the boundaries. Methods In this research, a Prompt Multi Level Segmentation Denoising model with a Fragile Correlated Feature Subset (PMLSD-FCFS) model is proposed for accurate denoising of MRI images and to extract the most relevant features set by applying a feature dimensionality reduction model for better brain tumor predictions. Results The proposed model achieves 98.2% accuracy in Multi-Level Image Segmentation and 98.4% accuracy in Fragile Correlated Feature Subset Generation. Conclusion The experimental findings indicated that the model proposed exhibits superior performance compared to the traditional algorithms. Furthermore, it successfully eliminates the noise from the MRI images, and most relevant features are only considered for brain tumor detection, thereby enhancing the accuracy of classification.

Optimized brain tumor identification via graph sample and aggregate-attention network with Artificial Lizard Search Algorithm

A brain tumour is an abnormal growth of brain nerves that interferes with normal brain function.It causes a great deal of deaths. Timely detection and treatment are essential for saving the lives from this illness. Locating tumor-affected brain cells is a tedious, time-consuming process. In this manuscript, a Graph Sample and Aggregate-Attention Network with Artificial Lizard Search Optimization Algorithm for identification of brain tumour (GSAAN-ALSOA-BTI) is proposed. Initially, the input imageries are obtained from Figshare Brain Tumor Dataset. The images are preprocessed using Data-Adaptive Gaussian Average Filtering (DAGAF) to eliminate the unnecessary noise from image frames. The pre-processed image is given into the Multi kernel k-Means clustering (MKKMC) for image segmentation to identify boundaries in an image. Finally, the extracted feature features are given to Graph Sample with Aggregate-Attention Network (GSAAN) for categorizing the brain tumour identification as benign and malignant tumors. In general, GSAAN does not express any adaption of optimization techniques for determining the ideal parameters to assure exact classification of brain tumour identification. Thus, the Artificial Lizard Search Optimization Algorithm (ALSOA) is proposed to improve the weight parameter of GSAAN, which accurately categorizes the brain tumour identification of brain tumor. The proposed model is executed in Python and its efficacy is evaluated utilizing performance metrics like accuracy, precision, recall, FI-score, error rate, computation time and ROC. The GSAAN-ALSOA-BTI method provides higher accuracy, higher precision, higher recall compared with existing methods.

Neuro-XAI: Explainable deep learning framework based on deeplabV3+ and bayesian optimization for segmentation and classification of brain tumor in MRI scans

The prevalence of brain tumor disorders is currently a global issue. In general, radiography, which includes a large number of images, is an efficient method for diagnosing these life-threatening disorders. The biggest issue in this area is that it takes a radiologist a long time and is physically strenuous to look at all the images. As a result, research into developing systems based on machine learning to assist radiologists in diagnosis continues to rise daily. Convolutional neural networks (CNNs), one type of deep learning approach, have been pivotal in achieving state-of-the-art results in several medical imaging applications, including the identification of brain tumors. CNN hyperparameters are typically set manually for segmentation and classification, which might take a while and increase the chance of using suboptimal hyperparameters for both tasks. Bayesian optimization is a useful method for updating the deep CNN's optimal hyperparameters. The CNN network, however, can be considered a "black box" model because of how difficult it is to comprehend the information it stores because of its complexity. Therefore, this problem can be solved by using Explainable Artificial Intelligence (XAI) tools, which provide doctors with a realistic explanation of CNN's assessments. Implementation of deep learning-based systems in real-time diagnosis is still rare. One of the causes could be that these methods don't quantify the Uncertainty in the predictions, which could undermine trust in the AI-based diagnosis of diseases. To be used in real-time medical diagnosis, CNN-based models must be realistic and appealing, and uncertainty needs to be evaluated. So, a novel three-phase strategy is proposed for segmenting and classifying brain tumors. Segmentation of brain tumors using the DeeplabV3+ model is first performed with tuning of hyperparameters using Bayesian optimization. For classification, features from state-of-the-art deep learning models Darknet53 and mobilenetv2 are extracted and fed to SVM for classification, and hyperparameters of SVM are also optimized using a Bayesian approach. The second step is to understand whatever portion of the images CNN uses for feature extraction using XAI algorithms. Using confusion entropy, the Uncertainty of the Bayesian optimized classifier is finally quantified. Based on a Bayesian-optimized deep learning framework, the experimental findings demonstrate that the proposed method outperforms earlier techniques, achieving a 97 % classification accuracy and a 0.98 global accuracy.

Advancements in brain tumor identification: Integrating synthetic GANs with federated-CNNs in medical imaging analysis

Brain tumors are a significant health concern worldwide, necessitating accurate and timely diagnosis for effective treatment planning and management. However, conventional methods for brain tumor identification through medical imaging analysis often face challenges related to accuracy, efficiency, and privacy concerns. Current approaches may struggle with limited datasets, privacy regulations hindering data sharing, and the need for specialized expertise in interpreting medical images. Accurately identifying brain tumors is pivotal in diagnosis, treatment planning, and patient prognosis. This research proposes a novel approach for advancing brain tumor identification by integrating Synthetic Generative Adversarial Networks with federated convolutional neural Networks in medical imaging analysis. Federated-CNNs are a type of neural network architecture designed for federated learning scenarios. In federated learning, model training occurs locally on data distributed across multiple devices or institutions without exchanging raw data. Federated CNNs allow collaborative model training across these distributed datasets by aggregating local model updates rather than exchanging raw data. This approach ensures that sensitive data remain localized within each participating institution, thus addressing privacy concerns in medical imaging analysis. Our methodology harnesses the power of GANs to generate synthetic brain MRI images, addressing data scarcity issues commonly encountered in medical imaging datasets. These synthetic images are then utilized in conjunction with Federated-CNNs, enabling cooperative model training between many healthcare institutions while maintaining the anonymity and privacy of data. Moreover, integrating Federated CNNs ensures that sensitive medical imaging data remain localized within participating institutions, addressing data privacy concerns and fostering collaboration among medical professionals. The research advances medical imaging analysis by introducing a novel methodology that leverages existing technologies to improve brain tumor identification accuracy. Specifically, the feature extraction phase using DenseNet121, implemented in MATLAB, achieves an outstanding accuracy of 99.82 % and outperforms Various existing methods, including Inception-V3, ResNet-18, and GoogleNet, demonstrating the efficacy of our approach in capturing discriminative features from medical imaging data. This high accuracy underscores the potential of our methodology to enhance diagnostic accuracy and clinical decision-making in neurology and oncology. The research offers a promising avenue for further exploration and innovation in medical imaging analysis, with significant implications for improving patient outcomes and advancing healthcare practices.

MRI Brain Tumor Segmentation using Cuckoo-based Dimensionality Reduction and Ensemble Convolutional Neural Network

Background Brain tumor identification at an early stage is a challenging task that increases the lifetime of patients. Specialists' conclusions on recognizing brain tumors are difficult, as they are based on their theoretical knowledge. It takes a huge amount of time to diagnose the patient. Recently, research has suggested an automated technique that is dependent on convolutional neural networks. Medical pictures are a set of accumulations of data that are hard to store and process, expending broad registering time. The decreased infiltrated systems are normally utilized as an information pre-preparing venture to make the picture information less mind-boggling with the goal that high-dimensional information may be recognized by a fitting and apt low-dimensional portrayal. Objective This study proposes an optimization-based dimensionality reduction and brain tumor segmentation using ensemble convolutional neural networks in MRI images to enhance disease diagnosis and extend healthcare accessibility. Methods Cuckoo-based dimensionality reduction and Ensemble CNN are proposed to segment the tumor region . The cuckoo-based optimization search technique is used to reduce the dimensionality of MRI Brain Images to perform better segmentation. The proposed technique is evaluated on the BRATS database, which contains two datasets: the Leaderboard and Challenge datasets. The outcomes are estimated utilizing the Dice Similarity Coefficient (DSC), Positive Predictive Value (PPV), and Sensitivity. Results The Experimental analysis shows promising results on the leaderboard dataset and the BRATS Challenge dataset. The proposed method outperformed the leaderboard dataset with a greater 91% Dice Similarity Coefficient (DCE), 95% Positive Predictive Value, and 87% Sensitivity of High-Grade Glioma (HGG). Seventy-two percent Dice Similarity Coefficient (DCE), 70% Positive Predictive Value, and 93% Sensitivity of Low-Grade Glioma (LGG). 88% Dice Similarity Coefficient (DCE), 90% Positive Predictive Value, and 91% Sensitivity of combined High-Grade glioma and Low-Grade glioma. For the BRATS Challenge dataset, the proposed method provides a 92% Dice Similarity Coefficient (DCE), 93% Positive Predictive Value, and 95% Sensitivity of High-Grade Glioma (HGG). 86% Dice Similarity Coefficient (DCE), 88% Positive Predictive Value and 93% Sensitivity of Low-Grade glioma (LGG). 85% Dice Similarity Coefficient (DCE), 89% Positive Predictive Value, and 92% Sensitivity of combined High-Grade glioma and Low-Grade glioma. Conclusion In this study, MRI Brain tumor segmentation using Cuckoo-based dimensionality reduction and Ensemble Convolutional Neural Network is proposed. The cuckoo search algorithm used for dimensionality reduction is performed in MRI images to reduce the dimensions. We also compared two of the existing methods with our proposed method. The leaderboard dataset and challenge dataset have been discussed. The challenge dataset for HGG provided good results in terms of dice similarity coefficient and positive predictive value. The sensitivity alone gets reduced when compared with the CNN and random forest methods. Experimental analysis shows promising results on the leaderboard dataset and the BRATS Challenge dataset.

A Review on: Identification of Brain Tumor in MRI Images by the Use of SVM and Fuzzy C-Means Combination

A Review on: Identification of Brain Tumor in MRI Images by the Use of SVM and Fuzzy C-Means Combination

A COMPUTER-AIDED BRAIN GLIOMA IDENTIFICATION USING MRI BASED TEXTURE ANALYSIS

Brain gliomas are deadly tumors that are discovered after a stressful, lengthy, and difficult procedure. Radiology is a broad and varied field that detects brain tumors, but interpreting radiological pictures of the brain needs advanced training, experience, and subject-matter expertise. The endeavor is challenging due to the wide variation in brain tumor tissues among individuals and similar cases within normal tissues. Magnetic resonance imaging (MRI)-based computer-aided biomedical image processing solves the challenges associated with brain tumor localization and identification while simultaneously addressing the shortage of qualified radiologists. This study proposes a computer-aided brain glioma identification (CABGD) model for brain glioma identification using the texture analysis of brain MRIs. The proposed model makes use of machine learning classifiers and brain MRI data. There were 200 MRIs of both normal and glioma brains in the experimental dataset. Firstly, the MRI dataset was pre-processed to crop the MRIs, size equalization, and gray-level conversion. Next, noises were removed by applying filters. Two ROIs of the sizes (10 ×10) were taken on each MRI after the tumor region was segmented. After extracting COM texture features from each ROI, thirty optimal features were obtained through a compound supervised feature selection, blend of Fisher (FSHR), probabilistic model of error (POE), average correlation (AVC), and mutual information (MUI). The optimal feature brain MRI dataset was classified into glioma and normal brain by applying machine learning classifiers named Bayas Net (BN), logistic model tree (LMT), and partial decision tree (PART); the classification results of the NB, LMT, and PART classifiers were 79.75%, 82.75, and 85%, respectively.

Brain Tumor Detection and Classification Using Adjusted InceptionV3, AlexNet, VGG16, VGG19 with ResNet50-152 CNN Model

INTRODUCTION: Brain tumors have become a major global health concern, characterized by the abnormal growth of brain cells that can negatively affect surrounding tissues. These cells can either be malignant (cancerous) or benign (non-cancerous), with their impact varying based on their location, size and type. OBJECTIVE: Early detection and classification of brain tumors are challenging due to their complex and variable structural makeup. Accurate early diagnosis is crucial to minimize mortality rates. METHOD: To address this challenge, researchers proposed an optimized model based on Convolutional Neural Networks (CNNs) with transfer learning, utilizing architectures like Inception-V3, AlexNet, VGG16, and VGG19. This study evaluates the performance of these adjusted CNN models for brain tumor identification and classification using MRI data. The TCGA-LGG and The TCIA, two well-known open-source datasets, were employed to assess the model's performance. The optimized CNN architecture leveraged pre-trained weights from large image datasets through transfer learning. RESULTS: The refined ResNet50-152 model demonstrated impressive performance metrics: for the non-tumor class, it achieved a precision of 0.98, recall of 0.95, F1 score of 0.93, and accuracy of 0.94; for the tumor class, it achieved a precision of 0.87, recall of 0.92, F1 score of 0.88, and accuracy of 0.96. CONCLUSION: These results indicate that the refined CNN model significantly improves accuracy in classifying brain tumors from MRI scans, showcasing its potential for enhancing early diagnosis and treatment planning.

Adaptive Detection and Classification of Brain Tumour Images Based on Photoacoustic Imaging

A new imaging technique called photoacoustic imaging (PAI) combines the advantages of ultrasound imaging and optical absorption to provide structural and functional details of tissues. It has broad application prospects in the accurate diagnosis and treatment monitoring of brain tumours. However, the existing photoacoustic image classification algorithms cannot effectively distinguish benign tumours from malignant tumours. To address this problem, the YoLov8-MedSAM model is proposed in this research to provide precise and adaptable brain tumour identification and detection segmentation. Additionally, it employs convolutional neural networks (CNNs) to classify and identify tumours in order to distinguish between benign and malignant variations in PAI. The experimental results show that the method proposed in this study not only effectively detects and segments brain tumours of various shapes and sizes but also increases the accuracy of brain tumour classification to 97.02%. The method provides richer and more valuable diagnostic information to the clinic and effectively optimizes the diagnosis and treatment strategy of brain tumours.

Enhancing brain tumor detection in MRI with a rotation invariant Vision Transformer.

The Rotation Invariant Vision Transformer (RViT) is a novel deep learning model tailored for brain tumor classification using MRI scans. RViT incorporates rotated patch embeddings to enhance the accuracy of brain tumor identification. Evaluation on the Brain Tumor MRI Dataset from Kaggle demonstrates RViT's superior performance with sensitivity (1.0), specificity (0.975), F1-score (0.984), Matthew's Correlation Coefficient (MCC) (0.972), and an overall accuracy of 0.986. RViT outperforms the standard Vision Transformer model and several existing techniques, highlighting its efficacy in medical imaging. The study confirms that integrating rotational patch embeddings improves the model's capability to handle diverse orientations, a common challenge in tumor imaging. The specialized architecture and rotational invariance approach of RViT have the potential to enhance current methodologies for brain tumor detection and extend to other complex imaging tasks.

CNN-Based Segmentation and Detection of Brain Tumors MRI Images: A Review

In order to provide effective therapy and enhance patient outcomes, it is essential to diagnose brain tumors as early as possible and with high accuracy. Due to the fact that it provides very specific anatomical information, magnetic resonance imaging (MRI) is an extremely important tool for diagnosing brain tumors. The manual segmentation and identification of brain tumors from MRI images, on the other hand, would take a significant amount of time and are prone to human mistake. The use of convolutional neural networks, often known as CNNs, has become more popular as a resource for automating certain activities. The purpose of this review article is to investigate the current developments in CNN-based methods for the segmentation and identification of brain tumors in magnetic resonance imaging (MRI) images. We describe the most significant difficulties that are connected with the analysis of brain tumors using MRI, investigate the different CNN designs that are used for this purpose, and evaluate the performance metrics of these architectures. The purpose of this study is to offer a complete overview of the present state-of-the-art in CNN-based brain tumor analysis of MRI data, emphasizing both the promise and limits of this method.

FCM and CBAC based Brain Tumor Identification and Segmentation

A brain tumor are an abnormal growth of cells within the brain, forming a mass that can be either cancerous (malignant) or non-cancerous (benign). Despite their differences, both types of tumors can pose serious health risks. As these tumors grow, they can increase intracranial pressure, leading to potential brain damage. This increased pressure can result in various symptoms such as headaches, seizures, vision problems, and changes in cognitive function. The potential for life-threatening consequences makes early detection and treatment crucial. The objective of the research is to develop a system or algorithm capable of accurately identifying the presence of brain tumors within medical imaging data (CT or MRI scans) and subsequently segmenting the tumor regions from the surrounding healthy brain tissue. This research aims at building an automated multi stage reliable system for classifying MRI images as tumor or non-tumor images. However, the research aims to diagnose brain tumor by extracting the tumor region accurately. The main contribution of this work is to automatically segment the tumor region from the MRI brain images, using Fuzzy C-Means (FCM) Clustering and the Content-Based Active Contour (CBAC) method. The CBAC method helps to resolve the issues of saddle points and broken edges in the extracted tumor region.