Generative Adversarial Network (GAN) Based MRI Data Synthesis with Validation of Convolutional Networks, KNN

Abstract

Magnetic Resonance Imaging (MRI) is a technology mainly used for disease prediction and treatment. Practically, due to poor quality of MRI images, sometimes it is advised to repeat the scan test again which causes some unavoidable situations with increase of costs. Therefore, only the improvement of the quality of MRI images can give us the relief from these unnecessary problems. So, we need an automated supervised machine learning algorithm to generate high resolution data without more efforts. In this paper, some computational techniques like convolutional networks, K-Nearest Neighbor classifier, and Generative Adversarial Network (GAN) are applied on the MRI images to get the high-resolution based MRI images. The methodology follows medical image localization, detection, segmentation, and classification. The validation results on real data of MRI data fundamentally determines its usefulness and demonstrates the effectiveness in compared to state-of-the-art super-resolution techniques.