Leveraging AI for Automatic Classification of PCOS Using Ultrasound Imaging
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866915090247188480 |
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| author | Divekar, Atharva Sonawane, Atharva |
| author_facet | Divekar, Atharva Sonawane, Atharva |
| contents | The AUTO-PCOS Classification Challenge seeks to advance the diagnostic capabilities of artificial intelligence (AI) in identifying Polycystic Ovary Syndrome (PCOS) through automated classification of healthy and unhealthy ultrasound frames. This report outlines our methodology for building a robust AI pipeline utilizing transfer learning with the InceptionV3 architecture to achieve high accuracy in binary classification. Preprocessing steps ensured the dataset was optimized for training, validation, and testing, while interpretability methods like LIME and saliency maps provided valuable insights into the model's decision-making. Our approach achieved an accuracy of 90.52%, with precision, recall, and F1-score metrics exceeding 90% on validation data, demonstrating its efficacy. The project underscores the transformative potential of AI in healthcare, particularly in addressing diagnostic challenges like PCOS. Key findings, challenges, and recommendations for future enhancements are discussed, highlighting the pathway for creating reliable, interpretable, and scalable AI-driven medical diagnostic tools. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_01984 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Leveraging AI for Automatic Classification of PCOS Using Ultrasound Imaging Divekar, Atharva Sonawane, Atharva Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition I.4.9 The AUTO-PCOS Classification Challenge seeks to advance the diagnostic capabilities of artificial intelligence (AI) in identifying Polycystic Ovary Syndrome (PCOS) through automated classification of healthy and unhealthy ultrasound frames. This report outlines our methodology for building a robust AI pipeline utilizing transfer learning with the InceptionV3 architecture to achieve high accuracy in binary classification. Preprocessing steps ensured the dataset was optimized for training, validation, and testing, while interpretability methods like LIME and saliency maps provided valuable insights into the model's decision-making. Our approach achieved an accuracy of 90.52%, with precision, recall, and F1-score metrics exceeding 90% on validation data, demonstrating its efficacy. The project underscores the transformative potential of AI in healthcare, particularly in addressing diagnostic challenges like PCOS. Key findings, challenges, and recommendations for future enhancements are discussed, highlighting the pathway for creating reliable, interpretable, and scalable AI-driven medical diagnostic tools. |
| title | Leveraging AI for Automatic Classification of PCOS Using Ultrasound Imaging |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition I.4.9 |
| url | https://arxiv.org/abs/2501.01984 |