Classification model for infectious lung diseases using convolutional neural networks on web and mobile applications
Abstract
Accurate lung disease diagnosis in infected patients is critical for effective treatment. Tuberculosis, COVID-19, pneumonia, and lung opacity are infectious lung diseases with visually similar chest X-ray presentations. Human expertise can be susceptible to errors due to fatigue or emotional factors. This research proposes a real-time deep learning-based classification system for lung diseases. Three models of convolutional neural networks (CNNs) were deployed to classify lung illnesses from chest X-ray images: MobileNetV3, ResNet-50, and InceptionV3. To evaluate the effect of high interclass similarity, the models were evaluated in 3-class (Tuberculosis, COVID-19, normal), 4-class (lung opacity, tuberculosis, COVID-19, normal), and 5-class (tuberculosis, lung opacity, pneumonia, COVID-19, normal) modes. The best classification accuracy was attained by retraining MobileNetV3, which obtained 94% and 93.5% for 5-class and 4-class, respectively. InceptionV3 had the lowest accuracy (90%, 89%, 93% for 5-, 4-, and 3-class), while ResNet-50 performed best for the 3-class setting. These findings suggest MobileNetV3's potential for accurate lung disease diagnosis from chest X-rays despite the interclass similarity, supporting the adoption of computer-aided detection systems for lung disease classification.
Keywords
Chest X-ray; Classification; Convolutional neural networks; Diagnosis; Lung disease
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PDFDOI: http://doi.org/10.11591/ijeecs.v39.i1.pp410-424
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Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES).