XcepKNN: Leveraging Hybrid Deep Learning for Enhanced MRI-Based Brain Tumor Classification

Brain tumors, characterized by the uncontrolled growth of cells within the brain, pose a formidable challenge in medical diagnostics due to their complex nature and the critical need for precise intervention. Magnetic Resonance Imaging (MRI) plays a pivotal role in the early detection and classification of brain tumors, offering detailed insights into tumor types without invasive procedures. However, the manual interpretation of MRI scans is labor-intensive, subject to human error, and heavily dependent on the expertise of radiologists. These challenges underscore the urgent need for advanced computational tools that enhance diagnostic accuracy and efficiency. This paper introduces XcepKNN, a novel architecture that integrates a K-Nearest Neighbor (KNN) classifier within the Xception deep learning framework, specifically designed to improve the classification of brain tumor MRI images. The hybrid model leverages the depthwise separable convolutions of Xception to extract detailed features from MRI data, while the embedded KNN classifier utilizes these features to accurately identify and classify various brain tumor types. The fusion of these techniques facilitates a more nuanced analysis of MRI images, enhancing the model’s ability to distinguish between tumor categories with high precision. Our extensive validation on a dataset of 7,023 MRI images demonstrates that XcepKNN significantly outperforms traditional models in terms of accuracy, precision, recall, and F1 scores. By providing an open-source implementation, this study contributes to the field of medical image analysis, offering a reliable tool for researchers and clinicians alike to improve the diagnostic processes for brain tumors.