HNMD-CNN: A Hierarchical Narrowing Multi-Deep Convolutional Neural Network for Precision Glioma Classification in 3D MRI Images

Brain tumors, though rare, are significant health risks, often reaching critical stages before diagnosis. Gliomas, classified as high grade (HGG) and low grade (LGG), require early detection to reduce mortality. While two-dimensional imaging has improved diagnostic techniques, three-dimensional imaging provides a more comprehensive view. This research introduces the Hierarchical Narrowing Multi-Deep Convolutional Neural Network (HNMD-CNN), a novel method for classifying brain tumors using 3D MRI images. The HNMD-CNN employs a hierarchical narrowing filtering strategy inspired by radiologists' models. Initially, large filters identify the tumor area and extract general features, followed by smaller filters to focus on specific tumor characteristics. This approach optimizes feature extraction and representation, improving diagnostic accuracy. We conducted extensive experiments using 3D MRI images from the BraTS2018 and BraTS2019 datasets, demonstrating the HNMD-CNN's ability to enhance convergence speed and classification accuracy without auxiliary algorithms. Our method achieved a remarkable classification accuracy of 99.93%, representing a significant advancement in 3D imaging for glioma classification. This work provides a powerful tool for early detection and accurate diagnosis of gliomas.