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. 2022 Mar 30:2022:9036457.
doi: 10.1155/2022/9036457. eCollection 2022.

Lung Disease Classification in CXR Images Using Hybrid Inception-ResNet-v2 Model and Edge Computing

Chandra Mani Sharma  1 , Lakshay Goyal  2 , Vijayaraghavan M Chariar  1 , Navel Sharma  3
Affiliations

Lung Disease Classification in CXR Images Using Hybrid Inception-ResNet-v2 Model and Edge Computing

Chandra Mani Sharma et al. J Healthc Eng. .

Retraction in

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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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Sample X-ray images from the dataset.
Figure 2
Figure 2
Label count of the classes.
Figure 3
Figure 3
Removing noise from the input image.
Figure 4
Figure 4
Data augmentation during training: (a) original, (b) rotated, (c) zoomed, (d) vertically shifted, and (e) horizontally shifted.
Figure 5
Figure 5
The architecture of the custom model.
Figure 6
Figure 6
Schematic flow diagram of the proposed system.
Figure 7
Figure 7
Layered architecture of the edge enabled system.
Figure 8
Figure 8
Accuracy and loss of comparison of (a) proposed custom model, (b) VGG19, (c) ResNet 50, and (d) MobileNetV2.
Figure 9
Figure 9
Mean accuracies of CNN and two different ML (SVM and decision tree)-based classifiers.
Figure 10
Figure 10
ROC curve.
Figure 11
Figure 11
Confusion matrix of the custom model.
See this image and copyright information in PMC

References

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