Review of Application YOLOv8 in Medical Imaging

Deep learning has revolutionized medical imaging analysis, with YOLOv8 emerging as a promising tool for
various tasks like lesion detection, organ segmentation and disease classification. This review investigates YOLOv8's
applications across diverse medical imaging modalities (X-Ray, CT-Scan and MRI). We conducted a systematic literature
search across databases like Pubmed, ScienceDirect and IEEE to identify relevant studies evaluating YOLOv8's
performance in medical imaging analysis. YOLOv8 achieved high performance for meningioma and pituitary tumors
with and without data augmentation (precision >0.92, recall >0.90, mAP >0.93). Glioma detection showed lower
performance but still promising results (precision >0.86, recall >0.81, mAP >0.86). Breast cancer detection with SGD
optimizer yielded best performance with an average mAP of 0.87 for mass detection. The model achieved high accuracy
in detecting normal (mAP 0.939) and malignant lesions (mAP 0.911). YOLO v8 on Dental radiograph successfully
detected cavities, impacted teeth, fillings and implants (precision of >0.82, recall of >0.78 and F1-Score of >0.80). Lastly,
for lung disease classification, YOLOv8 achieved high accuracy (99.8% training and 90% validation) in classifying
normal, COVID-19, influenza and lung cancer disease. With the importance to improve clinical decision-making and
patient outcomes in healthcare, the YOLOv8 algorthm underscores the importance of pre-processing, augmentation and
optimization of key hyperparameters.