A 5G network based conceptual framework for real-time malaria parasite detection from thick and thin blood smear slides using modified YOLOv5 model
Our model exemplifies how a generic one-stage object detection algorithm, such as YOLOv5x, can be repurposed to detect objects as small as malaria parasites from microscopic visuals in a cost-effective manner over the 5G network. By integrating the computational efficiency of deep learning with the connectivity of 5G networks, this system can significantly enhance remote diagnostic capabilities and contribute to smart healthcare solutions.
Authors
- Korhan Cengiz (ORCID: https://orcid.org/0000-0001-6594-8861)
- Ranjan Kumar Dash (ORCID: https://orcid.org/0000-0003-3482-465X)
- Adnan Akhunzada (ORCID: https://orcid.org/0000-0001-8370-9290)
- Nikola Ivković (ORCID: https://orcid.org/0000-0003-1730-2518)
- Swati Lipsa
Institutions
- Odisha University of Agriculture and Technology (IN)
- Prince Mohammad bin Fahd University (SA)
- University of Zagreb (HR)
- Qatar Science and Technology Park (QA)
- Odisha University of Technology and Research
Publication Details
- Journal
- Digital Health
- Published
- 2025-02-01
- DOI
- https://doi.org/10.1177/20552076251321540
- Citations
- 2
- Primary Topic
- Digital Imaging for Blood Diseases
- Type
- article
- Field-Weighted Citation Impact
- 2.55