Open Access
ARTICLE
Application of the Deep Convolutional Neural Network for the Classification of Auto Immune Diseases
Fayaz Muhammad1, Jahangir Khan1, Asad Ullah1, Fasee Ullah1, Razaullah Khan2, Inayat Khan2, Mohammed ElAffendi3, Gauhar Ali3,*
1 Department of Computer Science and Information Technology, Sarhad University of Science and Information
Technology, Peshawar, 25000, Pakistan
2 Department of Computer Science, University of Engineering and Technology, Mardan, 23200, Pakistan
3 EIAS Data Science and Blockchain Lab, College of Computer and Information Sciences, Prince Sultan University,
Riyadh, 11586, Saudi Arabia
* Corresponding Author: Gauhar Ali. Email:
(This article belongs to the Special Issue: Emerging Trends, Advances and Challenges of IoT in Healthcare and Education)
Computers, Materials & Continua 2023, 77(1), 647-664. https://doi.org/10.32604/cmc.2023.038748
Received 27 December 2022; Accepted 30 May 2023; Issue published 31 October 2023
Abstract
IIF (Indirect Immune Florescence) has gained much attention
recently due to its importance in medical sciences. The primary purpose of this
work is to highlight a step-by-step methodology for detecting autoimmune
diseases. The use of IIF for detecting autoimmune diseases is widespread in
different medical areas. Nearly 80 different types of autoimmune diseases
have existed in various body parts. The IIF has been used for image classification in both ways, manually and by using the Computer-Aided Detection
(CAD) system. The data scientists conducted various research works using
an automatic CAD system with low accuracy. The diseases in the human
body can be detected with the help of Transfer Learning (TL), an advanced
Convolutional Neural Network (CNN) approach. The baseline paper applied
the manual classification to the MIVIA dataset of Human Epithelial cells
(HEP) type II cells and the Sub Class Discriminant (SDA) analysis technique
used to detect autoimmune diseases. The technique yielded an accuracy of
up to 90.03%, which was not reliable for detecting autoimmune disease in the
mitotic cells of the body. In the current research, the work has been performed
on the MIVIA data set of HEP type II cells by using four well-known
models of TL. Data augmentation and normalization have been applied to
the dataset to overcome the problem of overfitting and are also used to
improve the performance of TL models. These models are named Inception
V3, Dens Net 121, VGG-16, and Mobile Net, and their performance can be
calculated through parameters of the confusion matrix (accuracy, precision,
recall, and F1 measures). The results show that the accuracy value of VGG-
16 is 78.00%, Inception V3 is 92.00%, Dense Net 121 is 95.00%, and Mobile
Net shows 88.00% accuracy, respectively. Therefore, DenseNet-121 shows
the highest performance with suitable analysis of autoimmune diseases. The
overall performance highlighted that TL is a suitable and enhanced technique compared to its counterparts. Also, the proposed technique is used to detect
autoimmune diseases with a minimal margin of errors and flaws.
Keywords
Cite This Article
APA Style
Muhammad, F., Khan, J., Ullah, A., Ullah, F., Khan, R. et al. (2023). Application of the deep convolutional neural network for the classification of auto immune diseases. Computers, Materials & Continua, 77(1), 647-664. https://doi.org/10.32604/cmc.2023.038748
Vancouver Style
Muhammad F, Khan J, Ullah A, Ullah F, Khan R, Khan I, et al. Application of the deep convolutional neural network for the classification of auto immune diseases. Comput Mater Contin. 2023;77(1):647-664 https://doi.org/10.32604/cmc.2023.038748
IEEE Style
F. Muhammad et al., "Application of the Deep Convolutional Neural Network for the Classification of Auto Immune Diseases," Comput. Mater. Contin., vol. 77, no. 1, pp. 647-664. 2023. https://doi.org/10.32604/cmc.2023.038748