Lung-Related Diseases Classification Using Deep Convolutional Neural Network

OLAYIWOLA, JOY OLUWABUKOLA and Badejo, J. A. and Okokpujie, Kennedy O. and Awomoyi, Morayo E. (2023) Lung-Related Diseases Classification Using Deep Convolutional Neural Network. Mathematical Modelling of Engineering Problems, 10 (4). pp. 1097-1104.

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Abstract

Accurate diagnosis is a crucial first step in the management and treatment of lung diseases, which include infectious diseases such as COVID-19, viral pneumonia, lung opacity, tuberculosis, and bacterial pneumonia. Despite these conditions sharing similar manifestations in chest X-ray images, it is imperative to correctly identify the disease present. This study, therefore, sought to develop a convolutional neural network (CNN)- based model for the classification of lung diseases. Four distinct CNN models, namely MobileNetV2, ResNet-50, ResNet-101, and AlexNet, were rigorously evaluated for their ability to classify lung diseases from chest X-ray images. These models were tested against three classification schemes to examine the impact of high interclass similarity: a 4-subclass classification (COVID-19, viral pneumonia, lung opacity, and normal), a 5-subclass classification (COVID-19, viral pneumonia, lung opacity, tuberculosis, and normal), and a 6-subclass classification (COVID-19, lung opacity, viral pneumonia, tuberculosis, bacterial pneumonia, and normal). The retrained ResNet-50 architecture yielded the best results, achieving a classification accuracy of 97.22%, 92.14%, and 96.08% for the 6-subclass, 5-subclass, and 4-subclass classifications respectively. Conversely, ResNet-101 demonstrated the lowest classification accuracy for the 6- subclass and 5-subclass classifications, with 78.12% and 79.49% respectively, while MobileNetV2 had the lowest accuracy for the 4-subclass classification, with 88.89%. These results suggest that, despite high interclass similarity, the ResNet-50 model can effectively classify lung-related diseases from chest X-ray images. This finding supports the use of computer-aided detection (CAD) systems as decision-support tools in the early classification of lung-related diseases.

Item Type:	Article
Uncontrolled Keywords:	deep learning, diagnosis, lung disease, MobileNetV2, ResNet-50, transfer learning
Subjects:	Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User:	nwokealisi
Date Deposited:	23 Jan 2024 14:42
Last Modified:	23 Jan 2024 14:42
URI:	http://eprints.covenantuniversity.edu.ng/id/eprint/17741

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