Automated Medical Image Analysis for Brain Tumour Detection Using DL

Abstract

Brain tumors are among the most critical neurological disorders, requiring timely and accurate diagnosis to improve treatment outcomes and patient survival. Conventional diagnostic procedures primarily rely on manual interpretation of Magnetic Resonance Imaging (MRI) scans by radiologists, which can be time-consuming and susceptible to inter-observer variability. Recent advances in Artificial Intelligence (AI) and Deep Learning (DL) have enabled the development of intelligent computer-aided diagnosis systems capable of assisting medical professionals in detecting brain abnormalities with high precision. This paper presents a deep learning-based framework for automated brain tumor detection and classification using MRI images. The proposed system integrates image preprocessing, data augmentation, convolutional feature extraction, and deep neural network classification to identify the presence of brain tumors and categorize them into clinically relevant classes. Image enhancement and normalization techniques are employed to improve the quality of MRI scans before feature learning, enabling the model to capture complex tumor characteristics while minimizing the effects of noise and intensity variations. The trained model is deployed through a secure web-based application that allows users to upload MRI images and receive prediction results with confidence scores in real time. Experimental evaluation demonstrates that the proposed framework achieves high classification accuracy, robustness, and computational efficiency while reducing diagnostic time and supporting reliable clinical decision-making. The proposed approach provides an intelligent, scalable, and user-friendly solution for early brain tumor diagnosis, offering significant potential for integration into modern healthcare systems and computer-aided radiology applications.