A Systematic Deep Learning Framework for Brain Tumor Detection and Classification

Uncontrolled growth of brain cells can lead to the formation of brain tumors, which are often fatal and significantly impact both the brain and the nervous system. Worldwide, brain tumors remain a major cause of mortality. Thus, accurate and timely detection is essential for effective treatment and minimizing the need for invasive medical procedures. However, manual identification and treatment of brain tumors is a challenging process, prone to inefficiencies and human error. Recent advances in deep learning have revolutionized brain tumor diagnosis, offering early detection and more precise outcomes. Considering the machine learning algorithms for accurate segmentation and classification, treatment processes can be significantly optimized. In this study, we employ two deep learning techniques: a 3D U-Net for Magnetic Resonance Imaging (MRI) image segmentation, followed by a Three-Dimensional Convolutional Neural Network (3D CNN) for tumor classification. The models were trained and validated using the BraTS 2019 dataset. Our result indicates a system accuracy of 98%, demonstrating the effectiveness of the proposed approach. This study highlights the potential of deep learning techniques to significantly enhance the precision of brain tumor diagnosis, offering a promising solution to the challenges posed by manual detection. It also indicates that this method outperforms existing techniques, providing an accurate and efficient tool for early tumor detection and treatment optimization.