A Deep Learning Fusion Approach to Diagnosis the Polycystic Ovary Syndrome (PCOS). (14th February 2023)

• Record Type: Journal Article
• Authors: Alamoudi, Abrar; Khan, Irfan Ullah; Aslam, Nida; Alqahtani, Nourah; Alsaif, Hind S.; Al Dandan, Omran; Al Gadeeb, Mohammed; Al Bahrani, Ridha
• Other Names: Ejbali Ridha Academic Editor.
• Is Part Of: Applied computational intelligence and soft computing. Volume 2023(2023)
• Publication Date: 2023-02-14
• Subjects: Computational intelligence -- Periodicals; Soft computing -- Periodicals; 006.305
• DOI: 10.1155/2023/9686697
• Languages: English
• ISSNs: 1687-9724

Abstract

Abstract : One of the leading causes of female infertility is PCOS, which is a hormonal disorder affecting women of childbearing age. The common symptoms of PCOS include increased acne, irregular period, increase in body hair, and overweight. Early diagnosis of PCOS is essential to manage the symptoms and reduce the associated health risks. Nonetheless, the diagnosis is based on Rotterdam criteria, including a high level of androgen hormones, ovulation failure, and polycystic ovaries on the ultrasound image (PCOM). At present, doctors and radiologists manually perform PCOM detection using ovary ultrasound by counting the number of follicles and determining their volume in the ovaries, which is one of the challenging PCOS diagnostic criteria. Moreover, such physicians require more tests and checks for biochemical/clinical signs in addition to the patient's symptoms in order to decide the PCOS diagnosis. Furthermore, clinicians do not utilize a single diagnostic test or specific method to examine patients. This paper introduces the data set that includes the ultrasound image of the ovary with clinical data related to the patient that has been classified as PCOS and non-PCOS. Next, we proposed a deep learning model that can diagnose the PCOM based on the ultrasound image, which achieved 84.81% accuracy using the Inception model. Then, we proposed a fusion model that includes the ultrasound image with clinical data to diagnose the patient if they have PCOS or not. The best model that has been developed achieved 82.46% accuracy by extracting the image features using MobileNet architecture and combine with clinical features.