Open AccessSynthetic image augmentation with generative adversarial network for enhanced performance in protein classification
Author(s) -
Rohit Verma,
R. Mehrotra,
Chinmay Rane,
Ritu Tiwari,
Arun Kumar Agariya
Publication year - 2020
Publication title -
biomedical engineering letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.709
H-Index - 26
eISSN - 2093-985X
pISSN - 2093-9868
DOI - 10.1007/s13534-020-00162-9
Subject(s) - computer science , artificial intelligence , task (project management) , image (mathematics) , generative adversarial network , field (mathematics) , generative grammar , macro , adversarial system , image quality , machine learning , pattern recognition (psychology) , quality (philosophy) , mathematics , engineering , philosophy , systems engineering , epistemology , pure mathematics , programming language
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 classification task, an increase of 2.7% in Macro F1 and 2.64% in Micro F1 score was observed. Our best results were obtained by the pretrained Inception V4 model that gave a fivefold cross-validated macro F1 of 0.603. The achieved results are contrasted with the existing work and comparisons show that the proposed method outperformed.