EOSA-GAN: Feature enriched latent space optimized adversarial networks for synthesization of histopathology images using Ebola optimization search algorithm

Generative adversarial networks (GAN) represent two deep learning (DL) models positioned in an adversarial manner to generate and evaluate images. This area of research promises to address several issues associated with medical image analysis using deep learning architectures and has been applied to medical image synthesis. The histopathology image samples are the gold standard for detecting and staging cancer since they contain rich latent information. However, this medical image modality is highly problematic: imbalanced class distribution in datasets, the rareness of publicly accessible pathologic findings, the burdensome task of image annotation, the increasing need to anonymize pathology samples, segmentation of regions of interest, and the demand for high-quality with super-resolution histopathology images. In this paper, we present a highly optimized, locally attenuated and two-level optimization strategy to improve the performance of GAN. First, a novel feature space-to-latent space mapping mechanism is designed to enrich the latent space of input to the generator. We applied a DL model to extract discriminant features and used dimensionality reduction to match the number of features to latent space. Secondly, a new metaheuristic algorithm, the Ebola optimization search algorithm (EOSA), optimizes the EOSA-GAN architecture and is experimentally applied to benchmark datasets. Results obtained showed that the quality of generated samples achieved an impressive outcome when evaluated using the Feature Similarity Indexing Method (FSIM), Peak Signal to Noise Ratio (PSNR), Structured Similarity Indexing Method (SSIM) and others. The finding from this study demonstrates the impact of optimization algorithms in stabilizing and speeding up GANs to convergence.