Deep Learning in Medical Image Classification from MRI-based Brain Tumor Images
This work addresses brain tumor detection for medical diagnosis, but it appears incremental as it primarily applies existing methods to a specific dataset.
The paper tackled brain tumor classification from MRI images using deep learning models, achieving results with five models including a new MobileNet-BT variant, but no concrete performance numbers were provided.
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.