A comparative study of fine-tuning deep learning models for MRI Images

Abstract

A brain tumor is an abnormal development of cells that reproduce uncontrollably and without any external stimulation. If tumors are not found early enough, brain tumors can be fatal to one’s health. Specialists and neurosurgeons employ magnetic resonance imaging (MRI) scans to diagnose brain tumors. Several deep learning methods for detecting the existence of brain tumors have been developed to overcome these constraints. The accurate detection of the size and location of a brain tumor is crucial in the diagnosis of the tumor. Medical image processing is a highly complex and tough discipline in which image processing and its methods are an active research topic. There are various technical deep learning and machine learning algorithms which are used to detect brain tumor. We used CNN architecture, ResNet, VGG16 and inception network in this paper and did a comparative study to find out the maximum accuracy for detecting brain tumor. When these algorithms are imposed to MRI images, the prediction of brain tumours is done quickly, and the higher accuracy aids in the treatment of patients. In this paper, after complete procedure and analysis of four different algorithms, we found out that CNN architecture is the most suitable with highest accuracy.