This study investigates the application of deep learning techniques for the detection of brain tumors in medical imaging, specifically focusing on magnetic resonance imaging (MRI) scans. With the increasing incidence of brain tumors and the critical need for timely diagnosis, leveraging advanced artificial intelligence methods has become paramount. This research employs various deep learning architectures, including convolutional neural networks (CNNs), to accurately identify and classify brain tumors from MRI images. The dataset comprises a diverse collection of annotated scans, enabling the model to learn intricate patterns associated with different tumor types. Performance metrics such as accuracy, sensitivity, specificity, and F1-score are utilized to evaluate the efficacy of the proposed models. Preliminary results demonstrate significant improvements in detection rates compared to traditional methods, indicating that deep learning can enhance diagnostic accuracy and support healthcare professionals in making informed decisions. This study contributes to the ongoing efforts to integrate artificial intelligence into medical diagnostics, ultimately aiming to improve patient outcomes through faster and more accurate brain tumor detection.

This study investigates the application of deep learning techniques for the detection of brain tumors in medical imaging, specifically focusing on magnetic resonance imaging (MRI) scans.