A Comprehensive Systematic Review of YOLO for Medical Object Detection (2018 to 2023)
Open accessAbstract
YOLO (You Only Look Once) is an extensively utilized object detection algorithm that has found applications in various medical object detection tasks. This has been accompanied by the emergence of numerous novel variants in recent years, such as YOLOv7 and YOLOv8. This study encompasses a systematic exploration of the PubMed database to identify peer-reviewed articles published between 2018 and 2023. The search procedure found 124 relevant studies that employed YOLO for diverse tasks including lesion detection, skin lesion classification, retinal abnormality identification, cardiac abnormality detection, brain tumor segmentation, and personal protective equipment detection. The findings demonstrated the effectiveness of YOLO in outperforming alternative existing methods for these tasks. However, the review also unveiled certain limitations, such as well-balanced and annotated datasets, and the high computational demands. To conclude, the review highlights the identified research gaps and proposes future directions for leveraging the potential of YOLO for medical object detection.
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YOLO is exceptionally well-suited for applications requiring rapid decision-making, such as autonomous driving, medical diagnostics, and surveillance systems.
“This capability makes YOLO exceptionally well-suited for applications requiring rapid decision-making, such as autonomous driving, medical diagnostics, and surveillance systems [8,9].”
From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [9]
Cites
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The PRISMA 2020 statement: an updated guideline for reporting systematic reviews
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Object Detection in 20 Years: A Survey
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“Deep learning-based object detection algorithms have shown exceptional performance in real-time object detection and localization [13]–[15].”
BackgroundA Review of Yolo Algorithm Developments
Jiang, P., Ergu, D., Liu, F., Cai, Y., & Ma, B. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.01.135 · Cited by 2,783 · see citing works
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Ali, M. L., & Zhang, Z. (2024) · Computers · doi:10.3390/computers13120336 · Cited by 383 · see citing works
- Authors
- Ragab, M. G., Abdulkadir, S. J., Muneer, A., Alqushaibi, A., Sumiea, E. H., Qureshi, R., Al-Selwi, S. M., & Alhussian, H.
- Affiliations
- Universiti Teknologi Petronas (MY)
- National University of Computer and Emerging Sciences (PK)
- ORCID
- Venue
- IEEE Access
- Published
- 2024
- Volume
- 12
- Pages
- 57815-57836
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W4394676676
- Cited by
- 306
- References
- 9
- Citations by year
- 2026: 105 · 2025: 162 · 2024: 39
- FWCI
- 10.75
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- IEEE Access (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Ministry of Higher Education (MOHE), Malaysia for providing financial assistance under Fundamental Research (FRGS/1/2022/ICT02/UTP/02/4)
- Universiti Teknologi PETRONAS under the Yayasan Universiti Teknologi PETRONAS (YUTP-FRG/015LC0-308)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- YOLO, medical object detection, YOLOv7, YOLOv8, lesion detection, skin lesion classification, cardiac abnormality detection, brain tumor segmentation, personal protective equipment detection, computational demands
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