Optimal deep convolution neural network for cervical cancer diagnosis model

Abstract

Biomedical imaging is an effective way of examining the internal organ of the human body and its diseases. An important kind of biomedical image is Pap smear image that is widely employed for cervical cancer diagnosis. Cervical cancer is a vital reason for increased women's mortality rate. Proper screening of pap smear images is essential to assist the earlier identification and diagnostic process of cervical cancer. Computer-aided systems for cancerous cell detection need to be developed using deep learning (DL) approaches. This study introduces an intelligent deep convolutional neural network for cervical cancer detection and classification (IDCNN-CDC) model using biomedical pap smear images. The proposed IDCNN-CDC model involves four major processes such as preprocessing, segmentation, feature extraction, and classification. Initially, the Gaussian filter (GF) technique is applied to enhance data through noise removal process in the Pap smear image. The Tsallis entropy technique with the dragonfly optimization (TE-DFO) algorithm determines the segmentation of an image to identify the diseased portions properly. The cell images are fed into the DL based SqueezeNet model to extract deep-learned features. Finally, the extracted features from SqueezeNet are applied to the weighted extreme learning machine (ELM) classification model to detect and classify the cervix cells. For experimental validation, the Herlev database is employed. The database was developed at Herlev University Hospital (Denmark). The experimental outcomes make sure that higher performance of the proposed technique interms of sensitivity, specificity, accuracy, and F-Score.