Integrated Deep Learning and Segmentation Approach for Brain Tumor Detection

Brain tumours, whether benign or malignant, pose a significant medical challenge, demanding early and precise identification for improved patient outcomes. Recent advances in deep learning algorithms have revolutionized medical image analysis, offering new possibilities for automated tumor detection. This research presents an innovative approach that integrates convolutional neural networks (CNNs) with sophisticated segmentation algorithms like BWT, aiming to enhance the precision and reliability of diagnosing brain tumours. Our approach employs the Biologically inspired BWT based on the human visual system’s ability to detect edges and contours in images within MRI scans and a deep CNN, utilizing state-of-the-art architectures like VGG and ResNet, to classify segmented regions as tumorous or healthy tissue. Furthermore, this study incorporates the u-net architecture to determine the specific location and features of the tumour, providing medical professionals with three-dimensional diagnostic help. This is especially useful given that the process of segmentation is difficult, repetitive, monotonous, and prone to errors. By combining these techniques, this hopes to significantly improve the accuracy and reliability of brain tumour identification, providing a reliable tool for medical practitioners. This study elaborates on our integrated methodology, assessment measures, and expected outcomes, with the aspiration to advance brain tumor detection techniques and elevate patient care in neurology and radiology