Intelligent Brain Tumour Diagnosis through Deep Segmentation and Vision Learning
Keywords:
Brain Tumor Detection, Medical Image Segmentation, Deep Learning, CNN, U-Net.Abstract
Brain tumor diagnosis is a critical task in medical imaging that requires high accuracy and early detection for effective treatment planning. Manual interpretation of MRI scans is time-consuming and prone to human error, motivating the development of automated deep learning-based diagnostic systems. This study proposes an Intelligent Brain Tumor Diagnosis framework based on Deep Segmentation and Vision Learning (IBTD-SVL). The model integrates deep convolutional segmentation networks with advanced vision learning architectures to accurately detect and classify brain tumors from MRI images. The segmentation module isolates tumor regions, while the classification module analyzes extracted features for tumor type prediction. The proposed system is evaluated on standard brain MRI datasets using performance metrics such as accuracy, precision, recall, F1-score, and Dice coefficient. Experimental results demonstrate improved tumor localization and classification accuracy compared to traditional CNN and segmentation-based approaches.
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