Brain Tumor Classification through MRIs using Transfer Learning with VGG16 Model

In the medical field, image segmentation is a crucial and difficult task. A useful technique for identifying aberrant brain tissue is magnetic resonance imaging (MRI) scans. For radiologists, correctly identifying and categorizing brain tumors from MRI scans is still a difficult and time-consuming task. This study offers a clever technique for accurately identifying brain tumors. The study investigates the use of Convolutional Neural Networks (CNNs) in conjunction with optimization techniques to classify different types of brain tumors from MRI data. In particular, tumor features are categorized and tumor kinds are identified using transfer learning on the VGG16 model. This method seeks to increase MRI scanning efficacy and improve identification precision. When evaluated using MRI scans from the Figshare, SARTAJ, and Br35H datasets [31], the proposed approach, which utilizes transfer learning, enhanced the performance of the original VGG16 model, allowing for more accurate and robust classification than its baseline capabilities, improving from 91.38% [1] to over 95%.