CystNet: An AI driven model for PCOS detection using multilevel thresholding of ultrasound images

被引:0
|
作者
Moral, Poonam [1 ]
Mustafi, Debjani [1 ]
Mustafi, Abhijit [1 ]
Sahana, Sudip Kumar [1 ]
机构
[1] Birla Inst Technol, Dept Comp Sci & Engn, Ranchi 835215, India
来源
SCIENTIFIC REPORTS | 2024年 / 14卷 / 01期
关键词
Polycystic ovary syndrome; Follicles; Image classification; Convolutional autoencoder; EfficientNet; Image segmentation; POLYCYSTIC-OVARY-SYNDROME; ANDROGEN EXCESS; CRITERIA;
D O I
10.1038/s41598-024-75964-3
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Polycystic Ovary Syndrome (PCOS) is a widespread endocrinological dysfunction impacting women of reproductive age, categorized by excess androgens and a variety of associated syndromes, consisting of acne, alopecia, and hirsutism. It involves the presence of multiple immature follicles in the ovaries, which can disrupt normal ovulation and lead to hormonal imbalances and associated health complications. Routine diagnostic methods rely on manual interpretation of ultrasound (US) images and clinical assessments, which are time-consuming and prone to errors. Therefore, implementing an automated system is essential for streamlining the diagnostic process and enhancing accuracy. By automatically analyzing follicle characteristics and other relevant features, this research aims to facilitate timely intervention and reduce the burden on healthcare professionals. The present study proposes an advanced automated system for detecting and classifying PCOS from ultrasound images. Leveraging Artificial Intelligence (AI) based techniques, the system examines affected and unaffected cases to enhance diagnostic accuracy. The pre-processing of input images incorporates techniques such as image resizing, normalization, augmentation, Watershed technique, multilevel thresholding, etc. approaches for precise image segmentation. Feature extraction is facilitated by the proposed CystNet technique, followed by PCOS classification utilizing both fully connected layers with 5-fold cross-validation and traditional machine learning classifiers. The performance of the model is rigorously evaluated using a comprehensive range of metrics, incorporating AUC score, accuracy, specificity, precision, F1-score, recall, and loss, along with a detailed confusion matrix analysis. The model demonstrated a commendable accuracy of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$96.54\%$$\end{document} when utilizing a fully connected classification layer, as determined by a thorough 5-fold cross-validation process. Additionally, it has achieved an accuracy of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$97.75\%$$\end{document} when employing an ensemble ML classifier. This proposed approach could be suggested for predicting PCOS or similar diseases using datasets that exhibit multimodal characteristics.
引用
收藏
页数:19
相关论文
共 50 条
  • [31] Automatic Crack Detection From Pavement Images Using Fuzzy Thresholding
    Ahmed, Nouha Ben Cheikh
    Lahouar, Samer
    Souani, Chokri
    Besbes, Kamel
    2017 INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND DIAGNOSIS (ICCAD), 2017, : 528 - 533
  • [32] Automated hip dysplasia detection using novel FlexiLBPHOG model with ultrasound images
    Key, Sefa
    Kurum, Huseyin
    Esmez, Omer
    Baig, Abdul Hafeez
    Hajiyeva, Rena
    Dogan, Sengul
    Tuncer, Turker
    AIN SHAMS ENGINEERING JOURNAL, 2025, 16 (01)
  • [33] Enhancement of Medical Ultrasound Images Using Multiscale Discrete Shearlet Transform Based Thresholding
    Gupta, Deep
    Anand, R. S.
    Tyagi, Barjeev
    2012 INTERNATIONAL SYMPOSIUM ON ELECTRONIC SYSTEM DESIGN (ISED 2012), 2012, : 286 - 290
  • [34] Non-Homomorphic Technique for Despeckling of Medical Ultrasound Images Using Curvelet Thresholding
    Girdhar, Akshay
    Gupta, Savita
    Bhullar, Jaskaran
    ADVANCED SCIENCE LETTERS, 2015, 21 (01) : 107 - 111
  • [35] Single Channel QRS Detection Using Wavelet And Median Denoising With Adaptive Multilevel Thresholding
    Modak, Sudipta
    Taha, Luay Yassin
    Abdel-Raheem, Esam
    2020 IEEE INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND INFORMATION TECHNOLOGY (ISSPIT 2020), 2020,
  • [36] Detection of pathologic liver using ultrasound images
    Santos, Jaime
    Silva, Jose Silvestre
    Santos, Andreia Andrade
    Belo-Soares, Pedro
    BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2014, 14 : 248 - 255
  • [37] Efficient detection of obstacles on tramways using adaptive multilevel thresholding and region growing methods
    Wu, Cheng
    Qiang, Xiang
    Wang, Yiming
    Yan, Changsheng
    Zhai, Guangyao
    PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART F-JOURNAL OF RAIL AND RAPID TRANSIT, 2018, 232 (05) : 1375 - 1384
  • [38] International multicenter validation of AI-driven ultrasound detection of ovarian cancer
    Christiansen, Filip
    Konuk, Emir
    Ganeshan, Adithya Raju
    Welch, Robert
    Pales Huix, Joana
    Czekierdowski, Artur
    Leone, Francesco Paolo Giuseppe
    Haak, Lucia Anna
    Fruscio, Robert
    Gaurilcikas, Adrius
    Franchi, Dorella
    Fischerova, Daniela
    Mor, Elisa
    Savelli, Luca
    Pascual, Maria angela
    Kudla, Marek Jerzy
    Guerriero, Stefano
    Buonomo, Francesca
    Liuba, Karina
    Montik, Nina
    Alcazar, Juan Luis
    Domali, Ekaterini
    Pangilinan, Nelinda Catherine P.
    Carella, Chiara
    Munaretto, Maria
    Saskova, Petra
    Verri, Debora
    Visenzi, Chiara
    Herman, Pawel
    Smith, Kevin
    Epstein, Elisabeth
    NATURE MEDICINE, 2025, 31 (01) : 189 - 196
  • [39] INTEGRATING MR AND ULTRASOUND IMAGES FOR AI-BASED PROSTATE CANCER DETECTION IN TRANSRECTAL ULTRASOUND IMAGES: A COMPARATIVE ASSESSMENT WITH CLINICIANS
    Jahanandish, Hassan
    Vesal, Sulaiman
    Bhattacharya, Indrani
    Kornberg, Zachary
    Zhou, Steve Ran
    Sommer, Elijah Richard
    Choi, Moon Hyung
    Fan, Richard E.
    Rusu, Mirabela
    Sonn, Geoffrey A.
    JOURNAL OF UROLOGY, 2024, 211 (05): : E317 - E317
  • [40] Band Correction in Random Amplified Polymorphism DNA Images Using Hybrid Genetic Algorithms with Multilevel Thresholding
    Garate O, Carolina
    Pinninghoff J, M. Angelica
    Contreras A, Ricardo
    NEW CHALLENGES ON BIOINSPIRED APPLICATIONS: 4TH INTERNATIONAL WORK-CONFERENCE ON THE INTERPLAY BETWEEN NATURAL AND ARTIFICIAL COMPUTATION, IWINAC 2011, PART II, 2011, 6687 : 426 - 435