AI DRIVEN EMBRYO ANALYSIS FOR ENHANCING IVF SUCCESS RATE USINGDEEP LEARNING AND COMPUTER VISION
DOI:
https://doi.org/10.5281/zenodo.21376191Keywords:
Embryo Classification, Deep Learning, IVF, CNN, VGG16, Computer VisionAbstract
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.
References
I. Yusuf Abas Mohamed Umi Kalsom Yusof Iza Sazanita Isa and Murizah Mohd Zain An automated blastocyst grading system using convolutional neural network and transfer learning In 2023 IEEE 13th International Conference on Control System Computing and Engineering ICCSCE pages 202–207 IEEE 2023
II. Gargee Vaidya Shreya Chandrasekhar Ruchi Gajjar Nagendra Gajjar Deven Patel and Manish Banker Time series prediction of viable embryo and automatic grading in ivf using deep learning The Open Biomedical Engineering Journal 15 1 2021
III. Vardit Ravitsky and Sarah Kimmins The forgotten men rising rates of male infertility urgently require new approaches for its prevention diagnosis and treatment Biology of Reproduction 101 5 872–874 2019
IV. Huy Phuong Tran Linh Nguyen Hoang Tran Huyen Thi Dang Tuan Dinh Vu Dat Tan Trinh Bao The Pham and Vu Ngoc Thanh Sang A swot analysis of human and machine learning based embryo assessment IEEE Access 8 227466–227481 2020
V. Xiangli Niu Cassie T Wang Richard Li Ghassan Haddad and Weihua Wang Is day 7 culture necessary for in vitro fertilization of cryopreserved warmed human oocytes Reproductive Biology and Endocrinology 18 1–4 2020
VI. Luis Martínez Granados María Serrano Antonio González Utor Nereyda Ortíz Vicente Badajoz Enrique Olaya Nicolás Prados Montse Boada Jose A Castilla and Special Interest Group in Quality of ASEBIR Inter laboratory agreement on embryo classification and clinical decision conventional morphological assessment vs time lapse PLoS One 12 8 e0183328 2017
VII. Iza Sazanita Isa Umi Kalsom Yusof and Murizah Mohd Zain Image processing approach for grading ivf blastocyst A state of the art review and future perspective of deep learning based models Applied Sciences 13 2 1195 2023
VIII. Sebastiaan Mastenbroek Fulco Van Der Veen Abbas Aflatoonian Bruce Shapiro Patrick Bossuyt and Sjoerd Repping Embryo selection in ivf Human Reproduction 26 5 964–966 2011
IX. Sunita Tandulwadkar Madhuri Patil and Sejal Naik Optimising the outcome of embryo transfer Reproductive Health 2019
X. Demián Glujovsky Andrea Marta Quinteiro Retamar Cristian Roberto Alvarez Sedo Agustín Ciapponi Simone Cornelisse and Deborah Blake Cleavage stage versus blastocyst stage embryo transfer in assisted reproductive technology Cochrane Database of Systematic Reviews 5 2022
XI. Mark Ramaekers Christiaan GA Viviers Boris V Janssen Terese AE Hellström Lotte Ewals Kasper van der Wulp Joost Nederend Igor Jacobs Jon R Pluyter Dimitrios Mavroeidis et al Computer aided detection for pancreatic cancer diagnosis radiological challenges and future directions Journal of Clinical Medicine 12 13 4209 2023
XII. Xiaoxuan Liu Livia Faes Aditya U Kale Siegfried K Wagner Dun Jack Fu Alice Bruynseels Thushika Mahendiran Gabriella Moraes Mohith Shamdas Christoph Kern et al A comparison of deep learning performance against health care professionals in detecting diseases from medical imaging a systematic review and meta analysis The Lancet Digital Health 1 6 e271–e297 2019
XIII. Athanasios Kallipolitis Melina Tziomaka Dimitris Papadopoulos and Ilias Maglogiannis Explainable computer vision analysis for embryo selection on blastocyst images In 2022 IEEE EMBS International Conference on Biomedical and Health Informatics BHI pages 1–4 IEEE 2022
XIV. Shanshan Wang Cong Zhou Dan Zhang Lei Chen and Haixiang Sun A deep learning framework design for automatic blastocyst evaluation with multifocal images IEEE Access 9 18927–18934 2021
XV. F J M Shamrat M Khalid T M QadahM Farrash and H Alshanbari An explainable multi objective hybrid machine learning model for reducing heart failure mortality PeerJ Computer Science 11 e2682 2025
XVI. Shumaiya Akter ShammiPronab Ghosh Ananda Sutradhar F M Javed Mehedi Shamrat Mohammad Ali Moni and Thiago Eustaquio Alves de Oliveira Advances in artificial intelligence and blockchain technologies for early detection of human diseases IEEE Transactions on Computational Social Systems 12 1 210–237 2025
XVII. A Sutradhar S Akter F J M Shamrat P Ghosh X Zhou M Y I B Idris K Ahmed and M A Moni Advancing thyroid care An accurate trustworthy diagnostics system with interpretable ai and hybrid machine learning techniques Heliyon 10 17 2024
XVIII. Majdi Khalid F M Javed Mehedi Shamrat Hanan Alshanbari Majed Farrash and Thamir M Qadah A dynamic selection hybrid model for advancing thyroid care with boosting balancing method IEEE Access 12 78641–78656 2024
XIX. Rifai Chai Dadang Gunawan et al Optimizing cnn hyperparameters for blastocyst quality assessment in small datasets IEEE Access 10 88621–88631 2022
XX. Anika Rahman and Md Golam Rabiul Alam Explainable ai based maternal health risk prediction using machine learning and deep learning In 2023 IEEE World AI IoT Congress AIIoT pages 0013–0018 IEEE 2023
XXI. Faisal Kevin Alkindy Umi Kalsom Yusof and Murizah Mohd Zain An automated day 3 embryo grading based on morphological characteristics using cnn with transfer learning techniques In 2023 IEEE 13th International Conference on Control System Computing and Engineering ICCSCE pages 214–219 IEEE 2023
XXII. Malathi Eswaran P Balasubramanie and V Pradeepa Assessment of human blastocyst using deep learning algorithm In 2023 2nd International Conference on Edge Computing and Applications ICECAA pages 691–697 IEEE 2023
XXIII. Ihlab STORK Spatio Temporal non uniformity prediction for knowledge graph embedding https://github.com/ih lab/STORK 2019
XXIV. Mingle Xu Sook Yoon Alvaro Fuentes and Dong Sun Park A comprehensive survey of image augmentation techniques for deep learning Pattern Recognition 137 109347 2023
XXV. Kodali Radha Mohan Bansal and Rajeev Sharma Raw waveform based custom scalogram crnn in cardiac abnormality diagnosis IEEE Access 2024
XXVI. Chelluri Divakar Ramanu Harsha Kodali Radha Dulipalla Venkata Rao Nadakuditi Madhavi and Thiruveedhula Bharadwaj Explainable ai for cnnlstm network in pcg based valvular heart disease diagnosis In 2024 14th International Conference on Cloud Computing Data Science and Engineering Confluence pages 92–97 IEEE 2024
XXVII. Andreas Holzinger Georg Langs Helmut Denk Kurt Zatloukal and Heimo Müller Causability and explainability of artificial intelligence in medicine Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery 9 4 e1312 2019
XXVIII. Aditya Bhattacharya Applied Machine Learning Explainability Techniques Make ml models explainable and trustworthy for practical applications using lime shap and more Packt Publishing Ltd 2022
XXIX. Prudhvi Thirumalaraju Manoj Kumar Kanakasabapathy Charles L Bormann Raghav Gupta Rohan Pooniwala Hemanth Kandula Irene Souter Irene Dimitriadis and Hadi Shafiee Evaluation of deep convolutional neural networks in classifying human embryo images based on their morphological quality Heliyon 7 2 2021
XXX. Elena Payá Lorena Bori Adrián Colomer Marcos Meseguer and Valery Naranjo Automatic characterization of human embryos at day 4 post insemination from time lapse imaging using supervised contrastive learning and inductive transfer learning techniques Computer Methods and Programs in Biomedicine 221 106895 2022
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