Automated high precision PCOS detection through a segment anything model on super resolution ultrasound ovary images
Abstract PCOS (Poly-Cystic Ovary Syndrome) is a multifaceted disorder that often affects the ovarian morphology of women of their reproductive age, resulting in the development of numerous cysts on the ovaries. Ultrasound imaging typically diagnoses PCOS, which helps clinicians assess the size, shap...
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Nature Portfolio
2025-05-01
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| Online Access: | https://doi.org/10.1038/s41598-025-01744-2 |
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| author | S. Reka T. Suriya Praba Mukesh Prasanna Vanipenta Naga Nithin Reddy Rengarajan Amirtharajan |
| author_facet | S. Reka T. Suriya Praba Mukesh Prasanna Vanipenta Naga Nithin Reddy Rengarajan Amirtharajan |
| author_sort | S. Reka |
| collection | DOAJ |
| description | Abstract PCOS (Poly-Cystic Ovary Syndrome) is a multifaceted disorder that often affects the ovarian morphology of women of their reproductive age, resulting in the development of numerous cysts on the ovaries. Ultrasound imaging typically diagnoses PCOS, which helps clinicians assess the size, shape, and existence of cysts in the ovaries. Nevertheless, manual ultrasound image analysis is often challenging and time-consuming, resulting in inter-observer variability. To effectively treat PCOS and prevent its long-term effects, prompt and accurate diagnosis is crucial. In such cases, a prediction model based on deep learning can help physicians by streamlining the diagnosis procedure, reducing time and potential errors. This article proposes a novel integrated approach, QEI-SAM (Quality Enhanced Image – Segment Anything Model), for enhancing image quality and ovarian cyst segmentation for accurate prediction. GAN (Generative Adversarial Networks) and CNN (Convolutional Neural Networks) are the most recent cutting-edge innovations that have supported the system in attaining the expected result. The proposed QEI-SAM model used Enhanced Super Resolution Generative Adversarial Networks (ESRGAN) for image enhancement to increase the resolution, sharpening the edges and restoring the finer structure of the ultrasound ovary images and achieved a better SSIM of 0.938, PSNR value of 38.60 and LPIPS value of 0.0859. Then, it incorporates the Segment Anything Model (SAM) to segment ovarian cysts and achieve the highest Dice coefficient of 0.9501 and IoU score of 0.9050. Furthermore, Convolutional Neural Network – ResNet 50, ResNet 101, VGG 16, VGG 19, AlexNet and Inception v3 have been implemented to diagnose PCOS promptly. Finally, VGG 19 has achieved the highest accuracy of 99.31%. |
| format | Article |
| id | doaj-art-9e0b19a06e88447cb386e6b0774a6b41 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-9e0b19a06e88447cb386e6b0774a6b412025-08-20T03:10:13ZengNature PortfolioScientific Reports2045-23222025-05-0115111710.1038/s41598-025-01744-2Automated high precision PCOS detection through a segment anything model on super resolution ultrasound ovary imagesS. Reka0T. Suriya Praba1Mukesh Prasanna2Vanipenta Naga Nithin Reddy3Rengarajan Amirtharajan4School of Computing, SASTRA Deemed UniversitySchool of Computing, SASTRA Deemed UniversitySchool of Computing, SASTRA Deemed UniversitySchool of Computing, SASTRA Deemed UniversitySchool of Electrical and Electronics Engineering, SASTRA Deemed UniversityAbstract PCOS (Poly-Cystic Ovary Syndrome) is a multifaceted disorder that often affects the ovarian morphology of women of their reproductive age, resulting in the development of numerous cysts on the ovaries. Ultrasound imaging typically diagnoses PCOS, which helps clinicians assess the size, shape, and existence of cysts in the ovaries. Nevertheless, manual ultrasound image analysis is often challenging and time-consuming, resulting in inter-observer variability. To effectively treat PCOS and prevent its long-term effects, prompt and accurate diagnosis is crucial. In such cases, a prediction model based on deep learning can help physicians by streamlining the diagnosis procedure, reducing time and potential errors. This article proposes a novel integrated approach, QEI-SAM (Quality Enhanced Image – Segment Anything Model), for enhancing image quality and ovarian cyst segmentation for accurate prediction. GAN (Generative Adversarial Networks) and CNN (Convolutional Neural Networks) are the most recent cutting-edge innovations that have supported the system in attaining the expected result. The proposed QEI-SAM model used Enhanced Super Resolution Generative Adversarial Networks (ESRGAN) for image enhancement to increase the resolution, sharpening the edges and restoring the finer structure of the ultrasound ovary images and achieved a better SSIM of 0.938, PSNR value of 38.60 and LPIPS value of 0.0859. Then, it incorporates the Segment Anything Model (SAM) to segment ovarian cysts and achieve the highest Dice coefficient of 0.9501 and IoU score of 0.9050. Furthermore, Convolutional Neural Network – ResNet 50, ResNet 101, VGG 16, VGG 19, AlexNet and Inception v3 have been implemented to diagnose PCOS promptly. Finally, VGG 19 has achieved the highest accuracy of 99.31%.https://doi.org/10.1038/s41598-025-01744-2Poly-cystic ovary syndromeEnhanced super resolution generative adversarial networksSegment anything modelConvolutional neural network |
| spellingShingle | S. Reka T. Suriya Praba Mukesh Prasanna Vanipenta Naga Nithin Reddy Rengarajan Amirtharajan Automated high precision PCOS detection through a segment anything model on super resolution ultrasound ovary images Scientific Reports Poly-cystic ovary syndrome Enhanced super resolution generative adversarial networks Segment anything model Convolutional neural network |
| title | Automated high precision PCOS detection through a segment anything model on super resolution ultrasound ovary images |
| title_full | Automated high precision PCOS detection through a segment anything model on super resolution ultrasound ovary images |
| title_fullStr | Automated high precision PCOS detection through a segment anything model on super resolution ultrasound ovary images |
| title_full_unstemmed | Automated high precision PCOS detection through a segment anything model on super resolution ultrasound ovary images |
| title_short | Automated high precision PCOS detection through a segment anything model on super resolution ultrasound ovary images |
| title_sort | automated high precision pcos detection through a segment anything model on super resolution ultrasound ovary images |
| topic | Poly-cystic ovary syndrome Enhanced super resolution generative adversarial networks Segment anything model Convolutional neural network |
| url | https://doi.org/10.1038/s41598-025-01744-2 |
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