Deep features to detect pulmonary abnormalities in chest X-rays due to infectious diseaseX: Covid-19, pneumonia, and tuberculosis

被引:45
|
作者
Biswas, Milon [1 ]
Gaur, Loveleen [2 ]
Alenezi, Fayadh [3 ]
Santosh, K. C. [4 ]
Mahbub, Md. Kawsher [1 ]
机构
[1] Bangladesh Univ Business & Technol, Mirpur 2, Dhaka 1216, Bangladesh
[2] Amity Univ, Gautam Buddha Nagar 201313, Uttar Pradesh, India
[3] Jouf Univ, Dept Elect Engn, Coll Engn, Sakakah 72238, Saudi Arabia
[4] Univ South Dakota, 2AI Appl Artificial Intelligence Res Lab Comp Sci, 414 E Clark St, Vermillion, SD 57069 USA
基金
美国国家科学基金会;
关键词
Chest X-ray; DNN; Medical imaging; Infectious DiseaseX; Covid-19; Pneumonia; Tuberculosis; SEGMENTATION;
D O I
10.1016/j.ins.2022.01.062
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
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页码:389 / 401
页数:13
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