Pap smear based cervical cancer detection using residual neural networks deep learning architecture

Abstract

AbstractDetecting and classifying the Pap smear cell images is important task for cervical cancer identification. In this article, dual tree complex wavelet transform (DTCWT) based modified deep learning algorithm is proposed for classifying the Pap smear cell images into four different cases normal, carcinoma in situ, dysplastic, and superficial. This proposed work consists of data augmentation module, DTCWT module and convolutional neural networks (CNN) module for the automatic classification of Pap smear cell images. The CNN module required huge number of cell images for obtaining high classification rate. Hence, the shearing and flipping functions are used in the data augmentation process to improve the dataset samples for further CNN classification module. Then, the spatial pixel behavior of data augmented images is transferred into multimodal pixels using DTCWT, which creates the sub band coefficients in matrix format. This matrix is trained and classified using ResNet 18 which classifies the source Pap smear cell image into four classes. This developed method is tested on the Pap smear cell images available in open access dataset. The average Pap smear detection index (PDI) of the automatic Pap smear cell image classification system is about 99%.