Comparison of various deep learning inpainting methods in smart colposcopy images Online publication date: Fri, 10-Jun-2022
by M.B. Jennyfer Susan; P. Subashini; M. Krishnaveni
International Journal of Computational Intelligence Studies (IJCISTUDIES), Vol. 11, No. 1, 2022
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%.
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