Open Access

ARTICLE

Intelligent Classification Model for Biomedical Pap Smear Images on IoT Environment

CSS Anupama¹, T. J. Benedict Jose², Heba F. Eid³, Nojood O Aljehane⁴, Fahd N. Al-Wesabi^5,*, Marwa Obayya⁶, Anwer Mustafa Hilal⁷
1 Department Electronics and Instrumentation Engineering, V. R. Siddhartha Engineering College, Vijayawada, 520007, India
2 Department of Computer Applications, Government Arts & Science College, Kanyakumari, 629401, India
3 Faculty of Science, AL-Azhar University, Cairo, 11651, Egypt
4 Faculty of Computers and Information Technology, University of Tabuk, 47512, Saudi Arabia
5 Department of Computer Science, College of Science & Art at Mahayil, King Khalid University, 62529, Saudi Arabia & Faculty of Computer and IT, Sana'a University, 31220, Yemen
6 Department of Biomedical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, 11564, Saudi Arabia
7 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Alkharj, 16278, Saudi Arabia
* Corresponding Author: Fahd N. Al-Wesabi. Email:

Computers, Materials & Continua 2022, 71(2), 3969-3983. https://doi.org/10.32604/cmc.2022.022701

Received 16 August 2021; Accepted 16 September 2021; Issue published 07 December 2021

Abstract

Biomedical images are used for capturing the images for diagnosis process and to examine the present condition of organs or tissues. Biomedical image processing concepts are identical to biomedical signal processing, which includes the investigation, improvement, and exhibition of images gathered using x-ray, ultrasound, MRI, etc. At the same time, cervical cancer becomes a major reason for increased women's mortality rate. But cervical cancer is an identified at an earlier stage using regular pap smear images. In this aspect, this paper devises a new biomedical pap smear image classification using cascaded deep forest (BPSIC-CDF) model on Internet of Things (IoT) environment. The BPSIC-CDF technique enables the IoT devices for pap smear image acquisition. In addition, the pre-processing of pap smear images takes place using adaptive weighted mean filtering (AWMF) technique. Moreover, sailfish optimizer with Tsallis entropy (SFO-TE) approach has been implemented for the segmentation of pap smear images. Furthermore, a deep learning based Residual Network (ResNet50) method was executed as a feature extractor and CDF as a classifier to determine the class labels of the input pap smear images. In order to showcase the improved diagnostic outcome of the BPSIC-CDF technique, a comprehensive set of simulations take place on Herlev database. The experimental results highlighted the betterment of the BPSIC-CDF technique over the recent state of art techniques interms of different performance measures.

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Keywords

Biomedical imaging; pap smear images; internet of things; deep learning; cervical cancer; disease diagnosis

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