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. 2023 Apr 14:1-13.
doi: 10.1007/s13369-023-07843-4. Online ahead of print.

Detection of Lung Opacity and Treatment Planning with Three-Channel Fusion CNN Model

Fuat Türk  1 , Yunus Kökver  2
Affiliations

Detection of Lung Opacity and Treatment Planning with Three-Channel Fusion CNN Model

Fuat Türk et al. Arab J Sci Eng. .

Abstract

Lung opacities are extremely important for physicians to monitor and can have irreversible consequences for patients if misdiagnosed or confused with other findings. Therefore, long-term monitoring of the regions of lung opacity is recommended by physicians. Tracking the regional dimensions of images and classifying differences from other lung cases can provide significant ease to physicians. Deep learning methods can be easily used for the detection, classification, and segmentation of lung opacity. In this study, a three-channel fusion CNN model is applied to effectively detect lung opacity on a balanced dataset compiled from public datasets. The MobileNetV2 architecture is used in the first channel, the InceptionV3 model in the second channel, and the VGG19 architecture in the third channel. The ResNet architecture is used for feature transfer from the previous layer to the current layer. In addition to being easy to implement, the proposed approach can also provide significant cost and time advantages to physicians. Our accuracy values for two, three, four, and five classes on the newly compiled dataset for lung opacity classifications are found to be 92.52%, 92.44%, 87.12%, and 91.71%, respectively.

Keywords: CNN; Deep learning; Lung opacity detection; Three-channel fusion CNN model.

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Figures

Fig. 1
Fig. 1
X-ray images with healthy and lung opacity [5] a healthy image and b lung opacity image
Fig. 2
Fig. 2
Flowchart for the diagnosis of Lung Opacity
Fig. 3
Fig. 3
Multi-class model architecture
Fig. 4
Fig. 4
Samples from dataset
Fig. 5
Fig. 5
Lung opacity mask extraction samples
Fig. 6
Fig. 6
Accuracy, loss, and learning rate values for the five-class classification process
Fig. 7
Fig. 7
Accuracy, loss, and learning rate values for the four-class classification process
Fig. 8
Fig. 8
Accuracy, loss, and learning rate values for the three-class classification process
Fig. 9
Fig. 9
Accuracy, loss, and learning rate values for the two-class classification process
Fig. 10
Fig. 10
Confusion matrix for the five-class dataset (0: COVID-19, 1: lung opacity, 2: normal, 3: pneumonia, 4: tuberculosis)
Fig. 11
Fig. 11
Confusion matrix for the four-class dataset (0: lung opacity, 1: normal, 2: pneumonia, 3: tuberculosis)
Fig. 12
Fig. 12
Confusion matrix for the three-class dataset (0: lung opacity, 1: normal, 2: tuberculosis)
Fig. 13
Fig. 13
Confusion matrix for the two-class dataset (0: lung opacity, 1: normal)
See this image and copyright information in PMC

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