A Comprehensive Systematic Review of YOLO for Medical Object Detection (2018 to 2023)

• Published in: IEEE access Vol. 12; pp. 57815 - 57836
• Main Authors: Ragab, Mohammed Gamal, Abdulkadir, Said Jadid, Muneer, Amgad, Alqushaibi, Alawi, Sumiea, Ebrahim Hamid, Qureshi, Rizwan, Al-Selwi, Safwan Mahmood, Alhussian, Hitham
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial intelligence; Biomedical imaging; Deep learning; Feature extraction; healthcare applications; Lesions; medical imaging; medical object detection; Medical services; Object detection; Object recognition; Real-time systems; Reviews; Surgery; systematic review; YOLO
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3386826

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.