Interpretation of Deep Learning Model in Embryo Selection for In Vitro Fertilization (IVF) Treatment
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866918132366442496 |
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| author | Kodali, Radha Dhulipalla, Venkata Rao Tatavarty, Venkata Siva Kishor Nadakuditi, Madhavi Thiruveedhula, Bharadwaj Gunnam, Suryanarayana Bavirisetti, Durga Prasad Reddy, Gogulamudi Pradeep |
| author_facet | Kodali, Radha Dhulipalla, Venkata Rao Tatavarty, Venkata Siva Kishor Nadakuditi, Madhavi Thiruveedhula, Bharadwaj Gunnam, Suryanarayana Bavirisetti, Durga Prasad Reddy, Gogulamudi Pradeep |
| contents | Infertility has a considerable impact on individuals' quality of life, affecting them socially and psychologically, with projections indicating a rise in the upcoming years. In vitro fertilization (IVF) emerges as one of the primary techniques within economically developed nations, employed to address the rising problem of low fertility. Expert embryologists conventionally grade embryos by reviewing blastocyst images to select the most optimal for transfer, yet this process is time-consuming and lacks efficiency. Blastocyst images provide a valuable resource for assessing embryo viability. In this study, we introduce an explainable artificial intelligence (XAI) framework for classifying embryos, employing a fusion of convolutional neural network (CNN) and long short-term memory (LSTM) architecture, referred to as CNN-LSTM. Utilizing deep learning, our model achieves high accuracy in embryo classification while maintaining interpretability through XAI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_06680 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Interpretation of Deep Learning Model in Embryo Selection for In Vitro Fertilization (IVF) Treatment Kodali, Radha Dhulipalla, Venkata Rao Tatavarty, Venkata Siva Kishor Nadakuditi, Madhavi Thiruveedhula, Bharadwaj Gunnam, Suryanarayana Bavirisetti, Durga Prasad Reddy, Gogulamudi Pradeep Computer Vision and Pattern Recognition Machine Learning Infertility has a considerable impact on individuals' quality of life, affecting them socially and psychologically, with projections indicating a rise in the upcoming years. In vitro fertilization (IVF) emerges as one of the primary techniques within economically developed nations, employed to address the rising problem of low fertility. Expert embryologists conventionally grade embryos by reviewing blastocyst images to select the most optimal for transfer, yet this process is time-consuming and lacks efficiency. Blastocyst images provide a valuable resource for assessing embryo viability. In this study, we introduce an explainable artificial intelligence (XAI) framework for classifying embryos, employing a fusion of convolutional neural network (CNN) and long short-term memory (LSTM) architecture, referred to as CNN-LSTM. Utilizing deep learning, our model achieves high accuracy in embryo classification while maintaining interpretability through XAI. |
| title | Interpretation of Deep Learning Model in Embryo Selection for In Vitro Fertilization (IVF) Treatment |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2506.06680 |