Automated Detection and Classification of Brain Tumors From MRI Images

Brain tumors, a significant health concern due to their potential to disrupt critical brain functions, require accurate and timely detection for effective management. Traditional diagnostic methods, reliant on manual examination of MRI scans by radiologists, often face challenges related to time constraints and susceptibility to human error. This study investigates the application of deep learning techniques to automate the detection and classification of brain tumors from MRI scans. We evaluated various neural network architectures, including CNN, VGG16, VGG19, and ResNet-50, to determine their effectiveness in identifying and classifying brain tumors from MRI images. Our results demonstrate that the CNN model outperforms VGG16, VGG19, and ResNet-50 in terms of both accuracy and generalization, making it the most effective choice for automated tumor detection. To address practical clinical needs, we developed a user-friendly web application that integrates the CNN model, enabling real-time tumor detection and classification. This application allows healthcare professionals to upload MRI images and receive immediate tumor detection and classification results, facilitating quicker and more precise diagnostic processes. The integration of deep learning models into this web-based platform marks notable progress in the automated detection of brain tumors. By enabling realtime analysis, the application supports clinicians in making informed decisions and planning treatment strategies with enhanced accuracy.