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Research Article: Alsibai & Heydari                                                             95


            5. Azziz R. PCOS: a diagnostic challenge. Reproductive   20. Deng  J,  Socher  R,  et  al.  ImageNet:  A  large-scale
               BioMedicine Online. 2004 April 5; 8(6): 644-648.      hierarchical  image  database.  IEEE  Conference  on
            6. Battaglia C, Mancini F, et al. Ultrasound evaluation of   Computer Vision and Pattern Recognition. 2009.
               PCO,  PCOS  and  OHSS.  Reproductive  BioMedicine   21. Sharma S, Mehra R. Conventional Machine Learning
               Online. 2004; 9(6): 614-619.                          and Deep Learning Approach for MultiClassification
            7. Wang  Y,  Ge  X,  et  al.  Deep  Learning  in  Medical   of  Breast  Cancer  Histopathology  Images—a
               Ultrasound Image Analysis: A Review. IEEE Access.     Comparative Insight. J Digit Imaging 2020, 33.
               2021; 9: 54310-54324.                              22. Gupta  A,  Ramanath  R.  Adam  vs.  SGD:  Closing  the
            8. Diaz-Escobar  J,  Ordorez-Guillen  NE,  et  al.  Deep-  generalization gap on image classification. OPT2021:
               learning  based  detection  of  COVID-19  using  lung   13th Annual Workshop on Optimization for Machine
               ultrasound  imagery.  PLoS  ONE  16(8):  e0255886.    Learning. .
               2021.                                              23. Adiwijaya, Unitary NW, Astuti W. Polycystic Ovary
            9. Carneiro  G,  Nascimento  JC,  Freitas  A.  The       Ultrasound  Images  Dataset.  [Online].;  Telkom
               Segmentation of the Left Ventricle of the Heart From   University  Dataverse  2021.  Available  from:
               Ultrasound Data Using Deep Learning Architectures     https://doi.org/10.34820/FK2/QVCP6V.
               and  Derivative-Based  Search  Methods.  IEEE
               Transactions on Image Processing. 2012; 21(3): 968-  Competing interests: Authors declare that they
               982.                                               have no competing interests  .
            10. Han Sea. A deep learning framework for supporting
               the  classification  of  breast  lesions  in  ultrasound
               images.  Physics  in  medicine  and  biology.  2017;   Data and materials availability: All data are
               62(19): 7714-7728.                                 available in the main text  .
            11. Hosain  AKMS,  Mehedi  MHK,  Kabir  IE.  PCONet:  A
               convolutional neural network architecture to detect
               polycystic  ovary  syndrome  (PCOS)  from  ovarian
               ultrasound images. arXiv preprint. 2022 Oct 2.
            12. Kaggle.    [Online].    Available     from:
               https://www.kaggle.com/datasets/anaghachoudha
               ri/pcos-detection-using-ultrasound-images.
            13. Lv  Wea.  Deep  Learning  Algorithm  for  Automated
               Detection  of  Polycystic  Ovary  Syndrome  Using
               Scleral Images. Frontiers in endocrinology. 2022 Jan.
               27; 12.
            14. S. Bharati, Podder P, et al.  Diagnosis of Polycystic
               Ovary   Syndrome   Using   Machine   Learning
               Algorithms.  2020  IEEE  Region  10  Symposium
               (TENSYMP). 2020;: 1486-1489.
            15. Srivastava S,  Kumar P, et al. Detection of Ovarian
               Cyst in Ultrasound Images Using Fine Tuned VGG 16
               Deep Learning Network. SN Computer Science. 2020.
            16. PyTorch. Documentation for DenseNet201. [Online].
               Available  from:  https://pytorch.org/vision/main
               /models/generated/torchvision.models.densenet20
               1.html.
            17. Steiner  A  et  al.  How  to  train  your  ViT?  Data,
               Augmentation,  and  Regularization  in  Vision
               Transformers.  Transactions  on  Machine  Learning
               Research;https://doi.org/10.48550/arXiv.2106.102.
            18. Huang  G  et  al.  Densely  Connected  Convolutional
               Networks. 2017 IEEE Conference on Computer Vision
               and Pattern Recognition (CVPR). 2016; 2261-2269.
            19. Morid MA, Borjali A, Del Fiol G. A scoping review of
               transfer learning research on medical image analysis
               using ImageNet. Computers in biology and medicine.
               2020; 128: 104115.

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