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Lung Disease Classification in CXR Images Using Hybrid Inception-ResNet-v2 Model and Edge Computing.
Sharma, Chandra Mani; Goyal, Lakshay; Chariar, Vijayaraghavan M; Sharma, Navel.
  • Sharma CM; Indian Institute of Technology Delhi, Delhi, India.
  • Goyal L; Veritas Technologies LLC, Pune, India.
  • Chariar VM; Indian Institute of Technology Delhi, Delhi, India.
  • Sharma N; Academic City College, Accra, Ghana.
J Healthc Eng ; 2022: 9036457, 2022.
Article in English | MEDLINE | ID: covidwho-1770049
ABSTRACT
Chest X-ray (CXR) imaging is one of the most widely used and economical tests to diagnose a wide range of diseases. However, even for expert radiologists, it is a challenge to accurately diagnose diseases from CXR samples. Furthermore, there remains an acute shortage of trained radiologists worldwide. In the present study, a range of machine learning (ML), deep learning (DL), and transfer learning (TL) approaches have been evaluated to classify diseases in an openly available CXR image dataset. A combination of the synthetic minority over-sampling technique (SMOTE) and weighted class balancing is used to alleviate the effects of class imbalance. A hybrid Inception-ResNet-v2 transfer learning model coupled with data augmentation and image enhancement gives the best accuracy. The model is deployed in an edge environment using Amazon IoT Core to automate the task of disease detection in CXR images with three categories, namely pneumonia, COVID-19, and normal. Comparative analysis has been given in various metrics such as precision, recall, accuracy, AUC-ROC score, etc. The proposed technique gives an average accuracy of 98.66%. The accuracies of other TL models, namely SqueezeNet, VGG19, ResNet50, and MobileNetV2 are 97.33%, 91.66%, 90.33%, and 76.00%, respectively. Further, a DL model, trained from scratch, gives an accuracy of 92.43%. Two feature-based ML classification techniques, namely support vector machine with local binary pattern (SVM + LBP) and decision tree with histogram of oriented gradients (DT + HOG) yield an accuracy of 87.98% and 86.87%, respectively.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 / Lung Diseases Type of study: Diagnostic study / Experimental Studies / Prognostic study Limits: Humans Language: English Journal: J Healthc Eng Year: 2022 Document Type: Article Affiliation country: 2022

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 / Lung Diseases Type of study: Diagnostic study / Experimental Studies / Prognostic study Limits: Humans Language: English Journal: J Healthc Eng Year: 2022 Document Type: Article Affiliation country: 2022