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Transfer Learning-Based Semi-Supervised Generative Adversarial Network for Malaria Classification

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submitted on 2024-08-19, 05:07 and posted on 2024-08-19, 05:10 authored by Ibrar Amin, Saima Hassan, Samir Brahim Belhaouari, Muhammad Hamza Azam

Malaria is a lethal disease responsible for thousands of deaths worldwide every year. Manual methods of malaria diagnosis are time-consuming that require a great deal of human expertise and efforts. Computer-based 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 malaria-infected and normal class) and achieved a classification accuracy of 96.6%.

Other Information

Published in: Computers, Materials & Continua
License: https://creativecommons.org/licenses/by/4.0
See article on publisher's website: https://dx.doi.org/10.32604/cmc.2023.033860

Funding

Open Access funding provided by the Qatar National Library.

History

Language

  • English

Publisher

Tech Science Press

Publication Year

  • 2023

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • Hamad Bin Khalifa University
  • College of Science and Engineering - HBKU

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