Improved medical image inpainting using automatic multi-task learning driven deep learning approach

Abstract

Distorted medical images can drastically reduce diagnosis accuracy using computer-aided diagnostic (CAD) systems. The objective of medical image classification is to improve diagnostic imaging precision and restore regions degraded by image inpainting. Deep Learning (DL) methods like Generative Adversarial Networks (GANs) are being used to solve these medical image inpainting issues. Recently, various improved GAN models were proposed for improved image inpainting, but challenges like computational inefficiency, unreliable restoration quality, and lack of correct analysis of discriminator block. To overcome these problems, a novel GAN model is proposed with modified Generator and Discriminator blocks for improved medical image inpainting. To improve image inpainting quality with minimum computational complexity, the Multi-Task Learning (MTL)-based generator block is designed. The MTL encoder is connected with three decoders for three tasks edge detection, organ boundary generation, and image completion. The MTL-based encoder and decoders are modified with an optimum number of layers and regularization functions to improve the outcomes. The output of the generator block is input into the discriminator block, where we designed the relevant feedback strategy to decrease generator loss by automatically changing the weights on wrong classifications. The simulation results revealed the efficiency of the proposed medical image inpainting approach and discriminator block performances over the state-of-the-art. Due to the modified MTL-based generator and discriminator block in the proposed approach, the PSNR is improved by 14.31 %, SSIM is improved by 8.56%, UQI is improved by 9.45%, and MSE is reduced by 22.78% using the proposed model compared to the state-of-the-arts.