Synthetic image augmentation with generative adversarial network for enhanced performance in protein classification.

ABSTRACT: Proteins are complex macromolecules accountable for the biological processes in the cell. In biomedical research, the images of protein are extensively used in medicine. The rate at which these images are produced makes it difficult to evaluate them manually and hence there exists a need to automate the system. The quality of images is still a major issue. In this paper, we present the use of different image enhancement techniques that improves the contrast of these images. Besides the quality of images, the challenge of gathering such datasets in the field of medicine persists. We use generative adversarial networks for generating synthetic samples to ameliorate the results of CNN. The performance of the synthetic data augmentation was compared with the classic data augmentation on the cl

SUBMITTER: Verma R 

PROVIDER: S-EPMC7438425 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature