The classification of brain tumors is a critical aspect of modern medical diagnostics. This study explores the application of Convolutional Neural Networks (CNNs), a subset of deep learning algorithms, in automating brain tumor classification. Brain tumors, with their potential for life-threatening consequences, demand accurate and timely diagnosis. CNNs have demonstrated their prowess in processing complex medical imaging data, particularly magnetic resonance imaging (MRI) and computed tomography (CT) scans, by effectively extracting intricate features and patterns. This research investigates the architecture, training, and performance of CNNs in this context, emphasizing the role of high-quality datasets and data preprocessing techniques. The study also addresses the challenges of model interpretability and the need for explainable AI in clinical settings. As a promising advancement in healthcare, CNNs offer the potential to enhance diagnostic accuracy, reduce human error, and expedite the decision-making process in brain tumor classification, ultimately leading to improved patient outcomes and more efficient healthcare systems.

The classification of brain tumors is a critical aspect of modern medical diagnostics.