DEEP LEARNING-BASED AUTOMATED DETECTION OF BRAIN TUMORS USING MRI SCANS AND 3D CONVOLUTIONAL NEURAL NETWORKS

Abstract

The early and accurate identification of brain abnormalities plays a vital role in improving patient outcomes and treatment planning [1], [2]. This project focuses on developing an intelligent medical image analysis system capable of detecting and classifying brain tumors automatically from MRI data [3], [4]. The proposed approach utilizes advanced three-dimensional convolutional neural network (3D-CNN) architectures that effectively capture spatial and contextual information from volumetric MRI images [5]–[7]. The system undergoes preprocessing steps such as skull stripping, normalization, and data augmentation to enhance input quality and model robustness [8], [9]. Through deep feature extraction and layer-wise learning, the model distinguishes between tumor and non-tumor regions with high precision [10], [11]. Experimental results demonstrate that the proposed deep learning framework outperforms conventional 2D models by leveraging 3D spatial relationships within the MRI scans [12]–[15]. This automated solution significantly reduces diagnostic time, assists radiologists in clinical decision-making, and contributes to improved brain healthcare through intelligent image-based diagnosis [16]–[19]. Furthermore, the integration of explainable AI techniques provides interpretability and transparency, which are crucial for clinical trust and real-world applicability [20], [25].