Software Development for Brain Glioma Detection Using Magnetic Resonance Imaging and Deep Learning Techniques

Abstract

The detection of brain gliomas is a crucial clinical challenge that requires early, accurate diagnostic methods to improve patient outcomes. This work presents the development of a deep learning-based system for glioma detection, employing an ensemble of ResNet18, VGG16, and DenseNet121 models trained with MRI images. The preprocessing involved dataset curation, image normalisation, and mask generation through K-means clustering. The trained model was integrated into a web application, allowing users to upload images and receive immediate diagnostic feedback. Experimental results demonstrate promising accuracy rates and reliable segmentation performance. This research highlights the potential of artificial intelligence (AI) to augment traditional medical imaging techniques and assist clinical diagnosis.