Open Access

ARTICLE

Optimal Deep Learning Based Inception Model for Cervical Cancer Diagnosis

by Tamer AbuKhalil¹, Bassam A. Y. Alqaralleh^2,*, Ahmad H. Al-Omari³

1 Computer Science Department, Faculty of Information Technology, Al Hussein bin Talal University, Ma'an, 71111, Jordan
2 MIS Department, College of Business Administration, University of Business and Technology, Jeddah, 21448, Saudi Arabia
3 Faculty of Science, Computer Science Department, Northern Border University, Arar, 91431, Saudi Arabia

* Corresponding Author: Bassam A. Y. Alqaralleh. Email:

Computers, Materials & Continua 2022, 72(1), 57-71. https://doi.org/10.32604/cmc.2022.024367

Received 15 October 2021; Accepted 20 December 2021; Issue published 24 February 2022

Abstract

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.

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Keywords

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