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@article{171897,
        author = {P. Malathi and Dr. M. Gomathi},
        title = {Deep learning classification of embryo quality based on Blastocyst},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {8},
        pages = {1284-1289},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171897},
        abstract = {A vital reproductive technique that has assisted millions in overcoming infertility issues is in vitro fertilization (IVF). The factors like environmental, clinical, and genetic are continue to vary due to complex interaction which affects its success rate. Although significant determinants of IVF success have been found using traditional statistical approaches, these methods frequently fail to capture the complex interactions that exist between these variables. Within this framework, artificial intelligence's deep learning subset provides a potent instrument for IVF decision-making and predictive modelling. This work investigates how deep learning techniques can be applied to enhance IVF outcome prediction by classifying excellent and bad embryos according to the existence of blastocysts. For the purpose of evaluating the viability of embryos, convolutional neural networks (CNNs) are utilized, especially transfer learning models like VGG19, MobileNetV2, InceptionV3 and Xception. Among these models MobileNetV2 performed well with the accuracy of 90% with low loss score of 0.21. Even in confusion metrices, MobileNetV2 achieved low false positive rate. Xception came in second with an accuracy of 87% and a loss score of 0.31. It also had a low false positive rate and a high true negative rate, indicating that it did a good job at classifying embryos of poor quality.},
        keywords = {In Vitro Fertilization, Embryos, Deep learning, Transfer Learning model.},
        month = {January},
        }