AI DRIVEN EMBRYO ANALYSIS FOR ENHANCING IVF SUCCESS RATE USINGDEEP LEARNING AND COMPUTER VISION - Dr. Md. Bakhar; Ayesha Adeeba (International Educational Journal of Science and Engineering, 2026)
Infertility is a growing concern in healthcare, and IVF is widely used for treatment. Success depends on accurate embryo selection. Traditional methods rely on manual observation, leading to inconsistency. This paper presents an AI-based embryo analysis system using deep learning and computer vision. A CNN model with VGG16 is used to classify embryo images into good and poor quality. A Flask-based interface enables real-time predictions. The system improves accuracy, reduces human bias, and enhances IVF outcomes.