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Fig. 4 | BMC Medical Informatics and Decision Making

Fig. 4

From: Assessment of a Grad-CAM interpretable deep learning model for HAPE diagnosis: performance and pitfalls in severity stratification from chest radiographs

Fig. 4

Displays the ROC curves of the edema grading model. MobileNet_V2 yielded favorable results for the three-class classification task (a), achieving a macroaverage ROC curve AUC of 0.92. The ROC curve AUC values for each class were as follows: class 0: 0.96, class 1: 0.84, class 2: 0.86, and class 3: 0.96. In the four-class classification task (b), MobileNet_V2 achieved a macroaverage ROC curve AUC of 0.89, with individual AUC values for class 0 (0.95), class 1 (0.79), class 2 (0.86), and class 3 (0.96)

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