Diagnosis of Polycystic Ovarian Syndrome (PCOS) Using Deep Learning

Banuki Nagodavithana; Abrar Ullah

doi:10.1007/978-981-19-9331-2_5

Diagnosis of Polycystic Ovarian Syndrome (PCOS) Using Deep Learning

Banuki Nagodavithana
, Abrar Ullah^*

^*Corresponding author for this work

School of Mathematical & Computer Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Citations (Scopus)

Abstract

Polycystic Ovarian Syndrome (PCOS) is a silent disorder that causes women to have weight gain, infertility, hair loss, and irregular menstrual cycles. It is a complex health issue, and one of the methods to diagnose patients with PCOS is to count the number of follicles in the ovaries. The issue with the traditional method is that it is time-consuming and prone to human errors as it can be challenging for medical professionals to distinguish between healthy ovaries and polycystic ovaries. Using Deep Learning, the concept was to create and use various Deep Learning Models such as a CNN, Custom VGG-16, ResNet- 50, and Custom ResNet-50, to obtain a high-accuracy result that will detect between healthy and polycystic ovaries. From the results and evaluation obtained, the CNN model achieved 99% accuracy, VGG 16 model: 58%, ResNet-50 Model: 58%, and Custom ResNet-50 Model: 96.7%.

Original language	English
Title of host publication	Proceedings of International Conference on Information Technology and Applications
Editors	Sajid Anwar, Abrar Ullah, Álvaro Rocha, Maria José Sousa
Publisher	Springer
Pages	47-61
Number of pages	15
ISBN (Electronic)	9789811993312
ISBN (Print)	9789811993305
DOIs	https://doi.org/10.1007/978-981-19-9331-2_5
Publication status	Published - 19 May 2023
Event	16th International Conference on Information Technology and Applications 2022 - Lisbon, Portugal Duration: 20 Oct 2022 → 22 Oct 2022

Publication series

Name	Lecture Notes in Networks and Systems
Volume	614
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	16th International Conference on Information Technology and Applications 2022
Abbreviated title	ICITA 2022
Country/Territory	Portugal
City	Lisbon
Period	20/10/22 → 22/10/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

ASJC Scopus subject areas

Control and Systems Engineering
Signal Processing
Computer Networks and Communications

Access to Document

10.1007/978-981-19-9331-2_5

Cite this

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title = "Diagnosis of Polycystic Ovarian Syndrome (PCOS) Using Deep Learning",

abstract = "Polycystic Ovarian Syndrome (PCOS) is a silent disorder that causes women to have weight gain, infertility, hair loss, and irregular menstrual cycles. It is a complex health issue, and one of the methods to diagnose patients with PCOS is to count the number of follicles in the ovaries. The issue with the traditional method is that it is time-consuming and prone to human errors as it can be challenging for medical professionals to distinguish between healthy ovaries and polycystic ovaries. Using Deep Learning, the concept was to create and use various Deep Learning Models such as a CNN, Custom VGG-16, ResNet- 50, and Custom ResNet-50, to obtain a high-accuracy result that will detect between healthy and polycystic ovaries. From the results and evaluation obtained, the CNN model achieved 99\% accuracy, VGG 16 model: 58\%, ResNet-50 Model: 58\%, and Custom ResNet-50 Model: 96.7\%.",

author = "Banuki Nagodavithana and Abrar Ullah",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 16th International Conference on Information Technology and Applications 2022, ICITA 2022 ; Conference date: 20-10-2022 Through 22-10-2022",

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doi = "10.1007/978-981-19-9331-2\_5",

language = "English",

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series = "Lecture Notes in Networks and Systems",

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pages = "47--61",

editor = "Sajid Anwar and Abrar Ullah and {\'A}lvaro Rocha and Sousa, \{Maria Jos{\'e}\}",

booktitle = "Proceedings of International Conference on Information Technology and Applications",

}

Nagodavithana, B & Ullah, A 2023, Diagnosis of Polycystic Ovarian Syndrome (PCOS) Using Deep Learning. in S Anwar, A Ullah, Á Rocha & MJ Sousa (eds), Proceedings of International Conference on Information Technology and Applications. Lecture Notes in Networks and Systems, vol. 614, Springer, pp. 47-61, 16th International Conference on Information Technology and Applications 2022, Lisbon, Portugal, 20/10/22. https://doi.org/10.1007/978-981-19-9331-2_5

Diagnosis of Polycystic Ovarian Syndrome (PCOS) Using Deep Learning. / Nagodavithana, Banuki; Ullah, Abrar.
Proceedings of International Conference on Information Technology and Applications. ed. / Sajid Anwar; Abrar Ullah; Álvaro Rocha; Maria José Sousa. Springer, 2023. p. 47-61 (Lecture Notes in Networks and Systems; Vol. 614).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Diagnosis of Polycystic Ovarian Syndrome (PCOS) Using Deep Learning

AU - Nagodavithana, Banuki

AU - Ullah, Abrar

PY - 2023/5/19

Y1 - 2023/5/19

N2 - Polycystic Ovarian Syndrome (PCOS) is a silent disorder that causes women to have weight gain, infertility, hair loss, and irregular menstrual cycles. It is a complex health issue, and one of the methods to diagnose patients with PCOS is to count the number of follicles in the ovaries. The issue with the traditional method is that it is time-consuming and prone to human errors as it can be challenging for medical professionals to distinguish between healthy ovaries and polycystic ovaries. Using Deep Learning, the concept was to create and use various Deep Learning Models such as a CNN, Custom VGG-16, ResNet- 50, and Custom ResNet-50, to obtain a high-accuracy result that will detect between healthy and polycystic ovaries. From the results and evaluation obtained, the CNN model achieved 99% accuracy, VGG 16 model: 58%, ResNet-50 Model: 58%, and Custom ResNet-50 Model: 96.7%.

AB - Polycystic Ovarian Syndrome (PCOS) is a silent disorder that causes women to have weight gain, infertility, hair loss, and irregular menstrual cycles. It is a complex health issue, and one of the methods to diagnose patients with PCOS is to count the number of follicles in the ovaries. The issue with the traditional method is that it is time-consuming and prone to human errors as it can be challenging for medical professionals to distinguish between healthy ovaries and polycystic ovaries. Using Deep Learning, the concept was to create and use various Deep Learning Models such as a CNN, Custom VGG-16, ResNet- 50, and Custom ResNet-50, to obtain a high-accuracy result that will detect between healthy and polycystic ovaries. From the results and evaluation obtained, the CNN model achieved 99% accuracy, VGG 16 model: 58%, ResNet-50 Model: 58%, and Custom ResNet-50 Model: 96.7%.

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M3 - Conference contribution

AN - SCOPUS:85161249389

SN - 9789811993305

T3 - Lecture Notes in Networks and Systems

SP - 47

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BT - Proceedings of International Conference on Information Technology and Applications

A2 - Anwar, Sajid

A2 - Ullah, Abrar

A2 - Rocha, Álvaro

A2 - Sousa, Maria José

PB - Springer

T2 - 16th International Conference on Information Technology and Applications 2022

Y2 - 20 October 2022 through 22 October 2022

ER -

Diagnosis of Polycystic Ovarian Syndrome (PCOS) Using Deep Learning

Abstract

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