Hybrid Optimization Algorithm-Based Generative Adversarial Network for Change Detection Using Pre-Operative and Post-Operative MRI

Abstract

Automatic detection of tumors is important to speed up treatment and to increase the survival rate of patients. In brain tumor detection, Magnetic Resonance Imaging (MRI) is considered an effective imaging model, which offers the internal structure of the brain. Change detection by pre-operative as well as post-operative multimodal images is an important research area in recent decades. Thus, this paper designs a hybrid optimization algorithm-based deep learning classifier to find the percentage of change detection in multimodal images. Initially, preprocessing is progressed to eradicate the noise from MRI images and then segmentation is performed using the modified DeepJoint model. After that, the pre-operative and the post-operative MRI images are engaged for the classification of a tumor. The classification of brain tumors is performed by Deep Convolutional Neural Network (Deep CNN) trained by a Tunicate Exponential Weighted Moving Average (TEWMA) algorithm, which is the integration of Tunicate Swarm Algorithm (TSA) and Exponential Weighted Moving Average (EWMA). After classification, the volume difference and the percentage of change detection are computed by GAN trained by PS-TEWMA, which is the integration of Particle Swarm Optimization (PSO) with TSA and EWMA. The proposed PS-TEWMA-based GAN obtained lower MSE and RMSE of 0.0881 and 0.2968 by measuring the volume detection. Also, it obtained minimal MSE and RMSE of 0.102 and 0.3194 concerning the percentage of change detection.