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In Information sciences

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 datasets, for non-healthy versus healthy CXR screening, the proposed DNN produced the following 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 versus Pneumonia, 98.99% on Covid-19 versus TB, and 100% on Pneumonia versus TB. To further 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.

Mahbub Md Kawsher, Biswas Milon, Gaur Loveleen, Alenezi Fayadh, Santosh K C


ACC, Accuracy, AI, Artificial Intelligence, AUC, Area Under the Curve, CADx, Computer-Aided Diagnosis, CNN, Convolutional Neural Network, CT, Computed Tomography, CXR, Chest X-ray, Chest X-ray, Covid-19, DL, Deep Learning, DNN, DNN, Deep Neural Network, Infectious DiseaseX, ML, Machine Learning, MTB, Mycobacterium Tuberculosis, Medical imaging, NN, Neural Network, Pneumonia, SEN, Sensitivity, SPEC, Specificity, TB, Tuberculosis, Tuberculosis, WHO, World Health Organization