TY - GEN
T1 - Diagnosis of Polycystic Ovarian Syndrome (PCOS) Using Deep Learning
AU - Nagodavithana, Banuki
AU - Ullah, Abrar
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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%.
UR - http://www.scopus.com/inward/record.url?scp=85161249389&partnerID=8YFLogxK
U2 - 10.1007/978-981-19-9331-2_5
DO - 10.1007/978-981-19-9331-2_5
M3 - Conference contribution
AN - SCOPUS:85161249389
SN - 9789811993305
T3 - Lecture Notes in Networks and Systems
SP - 47
EP - 61
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 -
