TY  - EJOU
AU  - AbuKhalil, Tamer 
AU  - Alqaralleh, Bassam A. Y. 
AU  - Al-Omari, Ahmad H. 

TI  - Optimal Deep Learning Based Inception Model for Cervical Cancer Diagnosis
T2  - Computers, Materials \& Continua

PY  - 2022
VL  - 72
IS  - 1
SN  - 1546-2226

AB  - Prevention of cervical cancer becomes essential and is carried out by the use of Pap smear images. Pap smear test analysis is laborious and tiresome work performed visually using a cytopathologist. Therefore, automated cervical cancer diagnosis using automated methods are necessary. This paper designs an optimal deep learning based Inception model for cervical cancer diagnosis (ODLIM-CCD) using pap smear images. The proposed ODLIM-CCD technique incorporates median filtering (MF) based pre-processing to discard the noise and Otsu model based segmentation process. Besides, deep convolutional neural network (DCNN) based Inception with Residual Network (ResNet) v2 model is utilized for deriving the feature vectors. Moreover, swallow swarm optimization (SSO) based hyperparameter tuning process is carried out for the optimal selection of hyperparameters. Finally, recurrent neural network (RNN) based classification process is done to determine the presence of cervical cancer or not. In order to showcase the improved diagnostic performance of the ODLIM-CCD technique, a series of simulations occur on benchmark test images and the outcomes highlighted the improved performance over the recent approaches with a superior accuracy of 0.9661.
KW  - Median filtering; convolutional neural network; pap smear; cervical cancer

DO  - 10.32604/cmc.2022.024367