Leveraging ResNet50 for Accurate Brain Tumor Diagnosis

Brain tumors are one of the foremost life-threatening diseases, producing serious complications that contribute to a heightened rate of morbidity and mortality. The emergence of deep learning strategies has vastly refined the process of identifying and categorizing brain tumors using MRI scans, providing crucial insights into the visualization of various structures within the human brain. Annually, countless individuals around the world receive a life-changing diagnosis of a brain tumor, and these cases often have low survival rates, underscoring the need for improved diagnostic approaches. This highlights the necessity for developing capable and systematic measures (improvement strategies) for diagnostics and treatment. While there has been progress in this regard, the search for a reliable detection system continues. In this paper, we focus on the ResNet50 model to enhance accuracy in detecting brain tumors. A type-curated ResNet50 is implemented for the brain tumor detection task, classifying MRI scans into two categories: 0 (Tumor) and 1 (No Tumor). Our findings reveal that the ResNet50 architecture successfully accomplished a classification accuracy of 97.3%, surpassing the VGG19 model, which attained an accuracy of 96%. This research seeks to contribute to the development of a robust and resilient model that enables healthcare professionals to make early and accurate decisions regarding brain tumors.