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Detection of pulmonary ground-glass opacity based on deep learning computer artificial intelligence

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Figure

Fig. 1 Steps of pulmonary parenchyma extraction. preliminary extraction of pulmonary contour; expansion; a Original CT image; b binarization of the CT image; c d filling airways; e contours corrosion; f contour mending and g pulmonary masking; h pulmonary parenchyma image
Fig. 3 Region of interest (ROI) superposition. pulmonary nodules. a ROI and red, green, blue (RGB) superposition effect of b ROI and RGB superposition effect of normal lung tissue
Fig. 4 Process of deep learning. ROI region of interest
Fig. 5 Sensitivity of AlexNet and GoogLeNet with different thresholds. When the threshold was higher than 0.5, the sensitivity of the AlexNet model was slightly better than that of the GoogLeNet model
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