With over 324,000 new cases and over 200,000 fatalities recorded each year, ovarian cancer (OC), the seventh most frequent disease in women and the most deadly gynecological malignancy, is a major global health concern. Due to this increasing worldwide burden Cancer prevention is one of the biggest public health issues of the twenty-first century. Ovarian disease diagnosis presents unique challenges that demand efficient and accurate detection technique. An automated system for ovarian cancer identification and categorization is suggested to assist physicians. This study explores application of YOLOv8 model for detecting and classifying objects in ovarian disease imaging datasets. Utilizing a curated dataset with 3,518 images divided across training, testing, and validation sets, model was trained over 50 epochs with advanced augmentation techniques including Blur, CLAHE, and Median Blur. The training process achieved significant detection performance, yielding good precision (mAP@50) and mAP@50-95. Comprehensive evaluation revealed class-specific challenges, including imbalances and variations in detection precision and recall rates. The integration of Tensor board visualizations further supports detailed performance analysis. The findings demonstrate YOLOv8's potential in advancing automated diagnostic tools for ovarian disease research and suggest that YOLOv8 can be used to predict ovarian cancer. Offering insights into future improvements in model optimization and dataset enhancement for clinical applications.
Jaware et al. (Wed,) studied this question.