An optimized transfer learning approach integrating deep convolutional feature extractors for malaria parasite classification in erythrocyte microscopy
The proposed ensemble learning approach highlights the potential of integrating transfer learning models to improve diagnostic accuracy for malaria detection. This scalable, automated solution reduces reliance on manual microscopy, making it highly applicable in resource-constrained settings and offering a significant advancement in malaria diagnostics.
Authors
- P. R. Anisha
- Jamel Baili (ORCID: https://orcid.org/0000-0001-5564-6114)
- C. Kishor Kumar Reddy (ORCID: https://orcid.org/0000-0002-3762-0137)
- Yongwon Cho (ORCID: https://orcid.org/0000-0001-8092-5799)
- Yunyoung Nam (ORCID: https://orcid.org/0000-0003-3082-6607)
- Rajasekhar Reddy Talla
- Ahlam Almushharaf
Institutions
- Princess Nourah bint Abdulrahman University (SA)
- Soonchunhyang University (KR)
- Stanley Medical College (IN)
- University of Business and Technology (SA)
- King Khalid University (SA)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-11-06
- DOI
- https://doi.org/10.3389/fmed.2025.1684973
- Primary Topic
- Digital Imaging for Blood Diseases
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Research Foundation
- Soonchunhyang University
- National Research Foundation of Korea
- Princess Nourah Bint Abdulrahman University
- King Khalid University
- Ministry of Science and ICT, South Korea