Screening Brain Tumors from MRI Imagesw with Deep Learning Approaches

Brain tumors are among the most life-threatening conditions, requiring precise and early diagnosis for effective treatment. Using 2064 photos, the accuracy of AlexNet, ResNet, VGC-16, and suggested model was compared. The test accuracy of the suggested CNN model was 89%, and validation correctness was 82%. A comparative study was performed after expanding the overall number of images to 5562. Prototype's test precision was 92.21% and the accuracy of validation was 89.77% on increasing the count of photos. Future iterations of the techniques can employ DTC classifier, Radial basis, and Soft-max to increase their precision.