期刊
INFORMATION SCIENCES
卷 592, 期 -, 页码 389-401出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2022.01.062
关键词
Chest X-ray; DNN; Medical imaging; Infectious DiseaseX; Covid-19; Pneumonia; Tuberculosis
Chest X-ray (CXR) imaging is a low-cost and easy-to-use method for diagnosing/screening pulmonary abnormalities caused by infectious diseases. A lightweight deep neural network (DNN) proposed in the study shows high accuracy in non-healthy versus healthy CXR screening, comparable to current state-of-the-art techniques.
Chest X-ray (CXR) imaging is a low-cost, easy-to-use imaging alternative that can be used to diagnose/screen pulmonary abnormalities due to infectious diseaseX: Covid-19, Pneumonia and Tuberculosis (TB). Not limited to binary decisions (with respect to healthy cases) that are reported in the state-of-the-art literature, we also consider non-healthy CXR screening using a lightweight deep neural network (DNN) with a reduced number of epochs and parameters. On three diverse publicly accessible and fully categorized data -sets, for non-healthy versus healthy CXR screening, the proposed DNN produced the fol-lowing accuracies: 99.87% on Covid-19 versus healthy, 99.55% on Pneumonia versus healthy, and 99.76% on TB versus healthy datasets. On the other hand, when considering non-healthy CXR screening, we received the following accuracies: 98.89% on Covid-19 ver-sus Pneumonia, 98.99% on Covid-19 versus TB, and 100% on Pneumonia versus TB. To fur-ther precisely analyze how well the proposed DNN worked, we considered well-known DNNs such as ResNet50, ResNet152V2, MobileNetV2, and InceptionV3. Our results are comparable with the current state-of-the-art, and as the proposed CNN is light, it could potentially be used for mass screening in resource-constraint regions.(c) 2022 Elsevier Inc. All rights reserved.
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