Deep Learning in Medical Image Classification from MRI-Based Brain Tumor Images

• Published in: IEEE International Conference on Power, Intelligent Computing and Systems (Online) pp. 840 - 844
• Main Authors: Liu, Xiaoyi, Wang, Zhuoyue
• Format: Conference Proceeding
• Language: English
• Published: IEEE 26.07.2024
• Subjects: Accuracy; Biomedical imaging; Brain modeling; brain tumor; Computational modeling; Computer architecture; computer vision; Decision making; Deep learning; Diseases; Image classification; Magnetic resonance imaging; medical image classification; transfer learning
• ISSN: 2834-8567
• DOI: 10.1109/ICPICS62053.2024.10796108

Brain tumors are among the deadliest diseases in the world. Magnetic Resonance Imaging (MRI) is one of the most effective ways to detect brain tumors. Accurate detection of brain tumors based on MRI scans is critical, as it can potentially save many lives and facilitate better decision-making at the early stages of the disease. Within our paper, four different types of MRI-based images have been collected from the database: glioma tumor, no tumor, pituitary tumor, and meningioma tumor. Our study focuses on making predictions for brain tumor classification. Five models, including four pre-trained models (MobileNet, EfficientNet-B0, ResNet-18, and VGG16) and one new model, MobileNet-BT, have been proposed for this study.