Computational Intelligence and Healthcare Informatics. Группа авторов

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- Tuberculosis [26] FHRNet ChestX-ray14 dataset - 50 Sigmoid - 14 chest pathologies [20] AG-CNN ChestX-ray14 50 Sigmoid - 14 chest pathologies [53] preact-ResNet [39_chp7_14] ChestX-ray14 - - Sigmoid - 8 chest pathologies out of 14 [70] Unified DCNN ChestX-ray8 - - Sigmoid - 8 chest pathologies out of 14 [28] Ensemble of AlexNet, VGG-16, VGG-19, ResNet-50, ResNet-101 and ResNet-152 Indiana dataset - - - - Cardiomegaly, Edema, and Tuberculosis [5] ChestNet CNN ChestX-ray14 14 30 Consolidation [49] CheXNeXt CNN ChestX-ray14 14 chest pathologies [46] Customized CNN NIH Tuberculosis Chest X-ray, Belarus Tuberculosis 23 ReLU Tuberculosis [61] Ensemble of RetinaNet and Mask R-CNN Kaggle dataset RSNA - - ReLU - Pneumonia [45] DarkCovid-Net COVID Chest x-ray Kaggle 17 ReLU - COVID, Normal and Pneumonia [65] GoogLeNet St. Michael’s Hospital chest x-ray - 60 ReLU - 5 pathologies: Cardiomegaly, Edema, Pleural effusion, pneumothorax, and consolidation [35] Ensemble of AlexNet and GoogleNet NIH Tuberculosis Chest X-ray, Belarus Tuberculosis - - - - Tuberculosis [31] ResNet101 Cohen and Kaggle [3] - - - - COVID-19–induced pneumonia

Table 2.3 Comparison of models on the basis of AUC score for 14 chest pathologies.

Ref.	Atel	Card	Effu	Infi	Mass	Nodu	Pne1	Pnet	Cons	Edem	Emph	Fibr	PT	Hern
[10]	0.784	0.888	0.831	0.705	0.838	0.796	0.727	0.876	0.746	0.852	0.942	0.837	0.796	0.912
[23]	0.795	0.887	0.875	0.703	0.835	0.716	0.742	0.863	0.786	0.892	0.875	0.756	0.774	0.836
[70]	0.716	0.807	0.784	0.609	0.706	0.671	0.633	0.806	0.708	0.835 Скачать книгу В начало < 17 18 19 20 21 22 23 24 25 26 > В конец e-mail: [email protected]