Medical MRI Image Segmentation to Enhance Tumor Treatment Using Deep Learning

Abstract

Segmentation of brain tumor images is one of the most essential and challenging tasks in the field of medical image processing, as human-assisted manual categorization can lead to erroneous prognosis and prediction. In addition, it is a difficult process when there is an abundance of data available to assist. The difficulty of extracting brain tumor regions from MRI images due to the wide diversity of brain tumor appearances and their similarity to normal tissues. In this paper, we propose a modified U-Net architecture within a deep learning framework for the detection and segmentation of brain lesions in MRI images. On authentic images from the Medical Image Computing and Computer-Assisted Interventions UW-Madison GI Tract Image Segmentation datasets, the applied model has been evaluated. Using the above-mentioned dataset, a test accuracy of 99.4% was attained. A comparison with other publications demonstrates that our U-Net-based model outperforms other deep learning-based models.