eprintid: 19415
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/94/15
datestamp: 2024-06-04 14:11:52
lastmod: 2024-06-04 14:11:52
status_changed: 2024-06-04 14:05:40
type: article
metadata_visibility: show
creators_name: Amin, I.
creators_name: Hassan, S.
creators_name: Belhaouari, S.B.
creators_name: Azam, M.H.
title: Transfer Learning-Based Semi-Supervised Generative Adversarial Network for Malaria Classification
ispublished: pub
keywords: Classification (of information); Computer aided diagnosis; Computer aided instruction; Deep learning; Diseases; Learning systems; Medical imaging; Medical problems; Transfer learning, Automated diagnosis; Human expertise; Learning models; Malaria; Malaria diagnosis; Manual methods; Performance; Semi-supervised; Transfer learning; VGG16, Generative adversarial networks
note: cited By 2
abstract: Malaria is a lethal disease responsible for thousands of deaths worldwide every year. Manual methods of malaria diagnosis are timeconsuming that require a great deal of human expertise and efforts. Computerbased automated diagnosis of diseases is progressively becoming popular. Although deep learning models show high performance in the medical field, it demands a large volume of data for training which is hard to acquire for medical problems. Similarly, labeling of medical images can be done with the help of medical experts only. Several recent studies have utilized deep learning models to develop efficient malaria diagnostic system, which showed promising results. However, the most common problem with these models is that they need a large amount of data for training. This paper presents a computer-aided malaria diagnosis system that combines a semi-supervised generative adversarial network and transfer learning. The proposed model is trained in a semi-supervised manner and requires less training data than conventional deep learning models. Performance of the proposed model is evaluated on a publicly available dataset of blood smear images (with malariainfected and normal class) and achieved a classification accuracy of 96.6. © 2023 Tech Science Press. All rights reserved.
date: 2023
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145346928&doi=10.32604%2fcmc.2023.033860&partnerID=40&md5=5af709176d85f55aca1e201e3a3d4382
id_number: 10.32604/cmc.2023.033860
full_text_status: none
publication: Computers, Materials and Continua
volume: 74
number: 3
pagerange: 6335-6349
refereed: TRUE
citation:   Amin, I. and Hassan, S. and Belhaouari, S.B. and Azam, M.H.  (2023) Transfer Learning-Based Semi-Supervised Generative Adversarial Network for Malaria Classification.  Computers, Materials and Continua, 74 (3).  pp. 6335-6349.