Publication detail

Attention-based Multiscale Deep Neural Network for Diagnosis of Polycystic Ovary Syndrome Using Ovarian Ultrasound Images

RASHID, S. KARNATI, M. AGGARWAL, G. DUTTA, M. SIKORA, P. BURGET, R.

Original Title

Attention-based Multiscale Deep Neural Network for Diagnosis of Polycystic Ovary Syndrome Using Ovarian Ultrasound Images

Type

conference paper

Language

English

Original Abstract

Polycystic Ovary Syndrome (PCOS) is a hormonal disorder that impacts a significant proportion of women in their reproductive years. It results in irregular menstrual cycles and elevated levels of androgens, known as male hormones. Women with PCOS often have ovaries that develop numerous small fluid-filled sacs called follicles, but they fail to release eggs regularly. While the precise cause of PCOS remains unknown, early identification and weight loss can help mitigate the risk of long-term complications. In this study, a novel attention-based multiscale convolutional neural network (AMCNN) is proposed for the detection of PCOS. The utilization of dilated convolution aids in preserving the multi-scale features with fewer parameters. The integration of multiscale characteristics is achieved by the attention mechanism, which enhances the importance of features within significant channels. The experimental results demonstrate the superior performance of the AMCNN, surpassing other prominent algorithms with an accuracy of 98.79%, proving its effectiveness for medical industrial applications.

Keywords

polycystic ovary syndrome, deep learning, convolutional neural networks, ultrasound images, diagnosis

Authors

RASHID, S.; KARNATI, M.; AGGARWAL, G.; DUTTA, M.; SIKORA, P.; BURGET, R.

Released

5. 12. 2023

Publisher

IEEE Computer Society

Location

Ghent

ISBN

979-8-3503-9328-6

Book

2023 15th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)

ISBN

2157-023X

Periodical

International Congress on Ultra Modern Telecommunications and Control Systems and Workshops

State

unknown

Pages from

44

Pages to

49

Pages count

6

URL

https://ieeexplore.ieee.org/document/10333275

BibTex

@inproceedings{BUT185578,
  author="Suzain {Rashid} and Mohan {Karnati} and Garmia {Aggarwal} and Malay Kishore {Dutta} and Pavel {Sikora} and Radim {Burget}",
  title="Attention-based Multiscale Deep Neural Network for Diagnosis of Polycystic Ovary Syndrome Using Ovarian Ultrasound Images",
  booktitle="2023 15th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)",
  year="2023",
  journal="International Congress on Ultra Modern Telecommunications and Control Systems and Workshops",
  pages="44--49",
  publisher="IEEE Computer Society",
  address="Ghent",
  doi="10.1109/ICUMT61075.2023.10333275",
  isbn="979-8-3503-9328-6",
  issn="2157-023X",
  url="https://ieeexplore.ieee.org/document/10333275"
}