Title: Comparison of various deep learning inpainting methods in smart colposcopy images

Authors: M.B. Jennyfer Susan; P. Subashini; M. Krishnaveni

Addresses: Department of Computer Science, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, India ' Department of Computer Science, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, India ' Department of Computer Science, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, India

Abstract: The specular reflection appears as white area on the cervical images that covers certain region of the images. It affects the quality of the cervical images causing difficulty for the physician to analyse the smart colposcopy images. In this paper, specular reflection is detected and removed from the cervical images, and these removed pixels are refilled using the inpainting methods. To fill the removed pixels, the deep learning inpainting algorithms like partial convolutional neural network, generative multicolumn convolutional neural network, and the dilated convolutional neural network to were used to get the complete and enhanced cervical images. The enhanced images are considered for lesion identification using the Bayes classifier. Based on the analysis, the partial convolution inpainting method gives higher quality with the PSNR value of 48.25 dB and SSIM value of 0.984. The enhanced images using the partial inpainting method identify the neoplasm with an accuracy of 98%.

Keywords: smart colposcope; specular reflection; lesion detection; convolutional neural network; inpainting; Bayes classifier.

DOI: 10.1504/IJCISTUDIES.2022.1004549

International Journal of Computational Intelligence Studies, 2022 Vol.11 No.1, pp.53 - 72

Received: 18 Jan 2022
Accepted: 19 Jan 2022

Published online: 07 Jun 2022 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article