Brain Tumor Detection From Mri Scans Using Convolutional Neural Networks: A Review

Abstract

Brain Tumor Detection Is One Of The Most Critical Applications Of Artificial Intelligence (AI) In Medical Image Analysis. Early And Accurate Identification Of Brain Tumors Plays A Vital Role In Improving Patient Survival Rates And Assisting Clinicians In Treatment Planning. Magnetic Resonance Imaging (MRI) Is Widely Used For Brain Tumor Diagnosis Because It Provides High-resolution Images Of Soft Tissues Without Exposing Patients To Ionizing Radiation. However, Manual Analysis Of MRI Scans Is Time-consuming, Labor-intensive, And Highly Dependent On The Expertise Of Radiologists. To Address These Challenges, Researchers Have Increasingly Adopted Deep Learning Techniques, Particularly Convolutional Neural Networks (CNNs), For Automated Brain Tumor Detection And Classification. This Review Paper Presents A Comprehensive Analysis Of Recent CNN-based Approaches For Brain Tumor Detection Using MRI Images. Various Stages Involved In Automated Tumor Detection, Including Image Acquisition, Preprocessing, Feature Extraction, Classification, And Performance Evaluation, Are Discussed In Detail. The Review Examines Different CNN Architectures Such As AlexNet, VGGNet, ResNet, DenseNet, EfficientNet, MobileNet, U-Net, And Vision Transformers That Have Been Successfully Applied To Brain Tumor Analysis. Furthermore, The Paper Compares Existing Methodologies Based On Datasets, Accuracy, Advantages, And Limitations. The Study Also Highlights Key Challenges Including Limited Dataset Availability, Class Imbalance, Overfitting, Computational Complexity, And Lack Of Model Interpretability. Emerging Research Trends Such As Transfer Learning, Explainable Artificial Intelligence (XAI), Federated Learning, And Multimodal Medical Imaging Are Discussed As Potential Solutions To These Challenges. The Findings Indicate That CNN-based Approaches Have Significantly Improved The Accuracy And Reliability Of Brain Tumor Detection Systems And Have Become Essential Components Of Modern Computer-aided Diagnosis (CAD) Frameworks. This Review Provides Valuable Insights For Researchers And Healthcare Professionals Interested In Developing Advanced AI-driven Brain Tumor Detection Systems.

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