Ovarian Cancer Detection Using Computer Vision

This study explores the application of artificial intelligence (AI) and deep learning in the field of computer vision, specifically for the detection of ovarian cancer. A computer vision model was developed, utilizing two different AI models, YOLOv8 and YOLOv7, to evaluate their effectiveness in this medical context. YOLOv8, being the current state-of-the-art model in computer vision, was chosen for its advanced capabilities, while YOLOv7 was selected for its established usage and performance record. Comparative analysis revealed that YOLOv8 outperformed YOLOv7 with a significantly higher accuracy rate of approximately 0.9. This enhanced accuracy is crucial in medical applications, particularly for early cancer detection which can substantially improve patient outcomes. Additionally, the model was benchmarked against other machine learning models and existing computer vision approaches in ovarian cancer detection. While this model demonstrated superior accuracy compared to other machine learning techniques, it was observed that certain other computer vision models, leveraging more customized architectures and larger datasets, achieved marginally better results. These findings indicate potential areas for future improvement of implemented model, including the integration of more comprehensive datasets and the refinement of model architecture. Furthermore, the research proposes the incorporation of additional health parameters to enhance the model's effectiveness and applicability in medical diagnostics.