PCOS-WaveConvNet: A Wavelet Convolutional  Neural Network for Polycystic Ovary Syndrome  Detection using Ultrasound images

Tiwari, Shamik; Maheshwari, Piyush

PCOS-WaveConvNet: A Wavelet Convolutional Neural Network for Polycystic Ovary Syndrome Detection using Ultrasound images

Date

2023

Authors

Tiwari, Shamik

Maheshwari, Piyush

Publisher

IEEE

Abstract

Women of reproductive age are susceptible to polycystic ovarian syndrome (PCOS), a hormonal condition. Multiple small follicles or cysts on the ovaries are one of the symptoms of PCOS and can be found using ultrasound imaging. Wavelet ConvNets have been applied in various applications, including image classification, object detection, and biomedical signal analysis. A Wavelet ConvNet is a type of deep learning model that applies wavelet transformation to input data before feeding it into a convolutional neural network. The wavelet transform is a mathematical technique that breaks down a signal or image into a series of sub-bands, each representing different frequency components of the original data. In this work, A 2D Discrete Wavelet Transform (2D-DWT) with the Haar wavelet is applied to each image. The resulting sub-bands namely Low-Low (LL), Low-High (LH), High-Low (HL), and High-High (HH) are concatenated to create a 4-channel feature map. Further, this concatenated feature map is fed into the ConvNet for classification. The PCOS-WaveConvNet classifier has attained 99.7% accuracy which is better than a usual ConvNet model.

Keywords

PCOS, Wavelet Transform, Haar Wavelet, ConvNet

Citation

Tiwari, S., Maheshwari, P. and 2023 9th International Conference on Information Technology Trends (ITT) Dubai, United Arab Emirates 2023 May 24 - 2023 May 25 (2023) “PCOS-WaveConvNet: A Wavelet Convolutional Neural Network for Polycystic Ovary Syndrome Detection using Ultrasound images,” in 2023 9th International Conference on Information Technology Trends (ITT), pp. 117–122.