Dr. P. Nagendra Kumar, Sk. Asiff, B. Poojitha, D. Ramesh 02 04 Embryo classification plays a crucial role in assisted reproductive technology ART especially in the context of in vitro fertilization IVF In India IVF has witnessed rapid growth with more than 1500 clinics performing approximately 25 lakh cycles annually as of 2023 according to the Indian Society of Assisted Reproduction Traditionally embryo assessment is performed manually by embryologists using the Gardner grading system which involves evaluating morphological characteristics such as blastocyst expansion inner cell mass and trophectoderm quality under a microscope However this method is inherently subjective often influenced by the embryologists experience fatigue and inconsistent application of grading criteria This subjectivity leads to significant inter and intraobserver variability and limits the accuracy and scalability of embryo selection contributing to stagnant IVF success rates of 3040 To address these challenges a hybrid deep learning model combining Convolutional Neural Networks CNN with a Cat Boost Classifier CBC is proposed This AIdriven approach aims to automate the classification of embryos into categories such as normal or viable thereby reducing human error and enhancing the predictive accuracy of implantation potential CNNs are used to extract detailed features from microscopic images of embryos by resizing and normalizing pixel values while the CBC performs efficient classification based on these features The model not only improves consistency but also significantly boosts performance achieving an accuracy of up to 9906 By enabling faster datadriven and objective decisionmaking the proposed system overcomes the limitations of manual evaluation It enhances embryo selection increases implantation success rates and offers a scalable solution for modern IVF practices in India Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026