Deep Neural Networks for Brain Tumor Detection from MRI Images

Md. Kawsher Mahbub, Milon Biswas, Md. Abdul Mozid Miah, M. Shamim Kaiser

Abstract: Brain tumor patients have a significant mortality rate. If the tumors are misdiagnosed, it may result in ineffective medical treatment and reduce their life chances. As the risk of brain tumors increases with age and the world’s population ages, there is an urgent need to develop low-cost, easy-to-use early detection technologies. MRI scans are commonly used to visualize a patient’s brain. Artificial intelligence (AI), deep learning (DL), and its sub-domain have recently reduced the need for human judgment in detecting disorders. DL models are increasingly being employed in traditional supervised learning algorithms due to their inherent advantages of automatically obtaining the required features from images. The detection of brain tumors is one of the most challenging tasks in biomedical imaging. This study is intended to propose a deep neural network (DNN) based solution with a limited number of epochs and parameters. The experiment was conducted on two different datasets, and the proposed DNN obtained 99.22% accuracy, 98.94% sensitivity, 99.53% specificity, 99.57% precision, and 99.26% F1-Score for Dataset (D1) and 99.43% accuracy, 98.86% sensitivity, 100.0% specificity, 100.0% precision, and 99.43% F1-Score for dataset (D2). The results are comparable with the current state-of-the-art.

External IDs:dblp:conf/tcce/MahbubBMK21