A Comparative Study of Brain Tumor Detection using Convolutional Neural Networks with MRI Images

This research emphasizes the global health challenge of brain tumors and emphasizes the importance of early detection using Convolutional Neural Networks (CNNs) on Magnetic Resonance Imaging (MRI). The dataset, including healthy and tumor MRI scans, underwent careful processing for CNN input. With a SoftMax Fully Connected layer, the CNN achieved 98% accuracy, outperforming Radial Basis Function (RBF) and Decision Tree (DT) classifiers. Feature extraction through clustering improved CNN accuracy, with the SoftMax classifier reaching 99.52% on test data. The study advances deep learning in medical image analysis, highlighting CNN-MRI synergy for precise brain tumor detection and potential advancements in early treatment and patient care.