IoMT-Based Automated Diagnosis of Autoimmune Diseases Using MultiStage Classification Scheme for Sustainable Smart Cities

Divya Biligere Shivanna, Thompson Stephan (), Fadi Al-Turjman, Manjur Kolhar and Sinem Alturjman
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Divya Biligere Shivanna: Department of Computer Science and Engineering, M. S. Ramaiah University of Applied Sciences, Bengaluru 560054, Karnataka, India
Thompson Stephan: Department of Computer Science and Engineering, M. S. Ramaiah University of Applied Sciences, Bengaluru 560054, Karnataka, India
Fadi Al-Turjman: Artificial Intelligence Engineering Department, AI and Robotics Institute, Near East University, Mersin 10, 99010 Nicosia, Turkey
Manjur Kolhar: Department of Computer Science, College of Arts and Science, Prince Sattam Bin Abdulaziz University, Al-Kharj 11990, Saudi Arabia
Sinem Alturjman: Research Center for AI and IoT, Faculty of Engineering, University of Kyrenia, Mersin 10, 99320 Kyrenia, Turkey

Sustainability, 2022, vol. 14, issue 21, 1-15

Abstract: The resolution of complex medical diagnoses using pattern recognition requires an artificial neural network-based expert system to automate autoimmune disease diagnosis in blood samples. This process is done using image-based computer-aided diagnosis (CAD) to reduce errors in the diagnosis process. This paper describes a Multistage Classification Scheme (MSCS), which uses antinuclear antibody (ANA) tests to identify and classify the existence of autoantibodies in the blood serum that bind to antigens found in the nuclei of mammalian cells. The MSCS classified HEp-2 cells into three stages by using Binary Tree (BT), Artificial Neural Network (ANN), and Support Vector Machine (SVM) as basic blocks. The Indirect Immunofluorescence (IIF) technique is used in the ANA test with Human Epithelial type-2 (HEp-2) cells as substrates. The efficiency of the proposed methodology is assessed using the dataset of ICPR 2016. The intermediate cells (IMC) and positive cells (PC) were separated in Stage 1 prior to preprocessing based on their total strength, and special preprocessing is applied to intermediate cells for improved output, and positive cells are subjected to mild preprocessing. The mean class accuracy (MCA) was 84.9% for intermediate cells and 95.8% for positive cells, although the carefully picked 24 features and SVM classifier were applied. ANN showed better performance by adjusting the weights using the SCGBP algorithm. So, the MCA is 88.4% and 97.1% for intermediate and positive cells, respectively. BT had an MCA of 95.3% for intermediate and 98.6% for positive. In Stage 2, the meta learners BT2, ANN2, and SVM2 were trained for an augmented feature set (24 + 3 results from base learners). Therefore, the performance of BT2, ANN2, and SV M2 was increased by 1.8%, 4.5%, and 4.1% as compared to Stage 1. In Stage 3, the final prediction was performed by majority voting among the results of the three meta learners to achieve 99.1% MCA. The proposed algorithm can be embedded into a CAD framework built for the ANA examination. The proposed model will improve operational efficiency, decrease medical expenses, expand accessibility to healthcare, and improve patient safety in the sector, enabling enterprises to lower unplanned downtime, develop new products or services, increase operational effectiveness, and enhance risk management.

Keywords: multistage classifier; binary tree; artificial neural network; support vector machine; classification; HEp-2; IoMT (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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