Computational Intelligence and Healthcare Informatics
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- Название:Computational Intelligence and Healthcare Informatics
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Computational Intelligence and Healthcare Informatics: краткое содержание, описание и аннотация
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The 21 chapters in this integrate several aspects of computational intelligence like machine learning and deep learning from diversified perspectives. The purpose of the book is to endow to different communities with their innovative advances in theory, analytical approaches, numerical simulation, statistical analysis, modeling, advanced deployment, case studies, analytical results, computational structuring and significance progress in healthcare applications.
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Table 2.2 Comparison of different deep learning models.
| Ref. | Model used | Dataset | No. layers | Epoch | Activation function | Iterations | Pathology detected |
|---|---|---|---|---|---|---|---|
| [23] | DenseNet-121 | ChestX-ray14 | 121 | - | Softmax | 50,000 | 14 chest pathologies |
| [67] | Pretrained CNNs: | ChestX-ray14 | - | 50 | - | - | 14 chest pathologies |
| [7] | VDSNet | ChestX-ray8 | - | - | ReLU | - | Pulmonary diseases |
| [10] | DualCheXNet | ChestX-ray14 | 169 | - | ReLU | - | 14 chest pathologies |
| [2] | CNN | ChestX-ray8 | 5 | 1,000–4,000 | ReLU | 40,000 | 12 chest pathologies out of 14 |
| BPNN | 3 | - | 5,000 | ||||
| CpNN | 2 | - | 1,000 | ||||
| [8] | Customized U-Net | ChestX-ray8 | 35 | 100–200 | ReLU | 20 | Cardiomegaly |
| [47] | Ensemble of DesnSeNet-121, DenseNet-169, DenseNet-201, Inception-ResNet-v2 Xception, NASNetLarge | CheXpert | 5 | Sigmoid | 50,000 | Only 5 pathologies: Atelactasis, Cardiomegaly, Pleural, Effusion, and Edema | |
| [51] | STN based CNN | Lung ultrasonography videos | - | - | ReLU | - | COVID-19 Pneumonia |
| [46] | Ensemble with AlexNet | NIH Tuberculosis Chest X-ray dataset [10.31] and Belarus Tuberculosis Portal dataset [10.32] | - | - | ReLU | - | 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 | 0.815 | 0.769 | 0.708 | 0.767 |
| [69] | 0.743 | 0.875 | 0.811 | 0.677 | 0.783 | 0.698 | 0.696 | 0.81 | 0.723 | 0.833 | 0.822 | 0.804 | 0.751 | 0.899 |
| [8] | - | 0.940 | - | - | - | - | - | - | - | - | - | - | - | - |
| [47] | 0.909 | 0.910 | 0.964 | - | - | - | - | - | 0.957 | 0.958 | - | - | - | - |
| [26] | 0.794 | 0902 | 0.839 | 0.714 | 0.827 | 0.727 | 0.703 | 0.848 | 0.773 | 0.834 | 0.911 | 0.824 | 0.752 | 0.916 |
| [20] | 0.853 | 0.939 | 0.903 | 0.754 | 0.902 | 0.828 | 0.774 | 0.921 | 0.842 | 0.924 | 0.932 | 0.964 | 0.837 | 0.921 |
| [74] AlexNet | 0.645 | 0.692 | 0.664 | 0.604 | 0.564 | 0.648 | 0.549 | 0.742 | - | - | - | - | - | - |
| GoogleNet | 0.631 | 0.706 | 0.688 | 0.609 | 0.536 | 0.558 | 0.599 | 0.782 | - | - | - | - | - | - |
| VGGNet-16 | 0.628 | 0.708 | 0.650 | 0.589 | 0.510 | 0.656 | 0.510 | 0.751 | - | - | - | - | - | - |
| ResNet50 | 0.707 | 0.814 | 0.736 | 0.613 | 0.561 | 0.716 | 0.63 | 0.789 | - | - | - | - | - | - |
| [41] | 0.762 | 0.883 | 0.816 | 0.679 | 0.801 | 0.729 | 0.709 | 0.838 | 0.744 | 0.841 | 0.884 | 0.800 | 0.754 | 0.876 |
| [49] | 0.862 | 0.831 | 0.901 | 0.721 | 0.909 | 0.894 | 0.851 | 0.944 | 0.893 | 0.924 | 0.704 | 0.806 | 0.798 | 0.851 |
| [13] | - | 0.875 | 0.962 | - | - | - | - | 0.861 | 0.850 | 0.868 | - | - | - | - |
Table 2.4 Comparison of DL models on the basis of different performance metrics.
| Ref. | Model | Accuracy | F1-Score | Specificity | Sensitivity | PPV | NPV | AUC | Recall | Precision |
|---|---|---|---|---|---|---|---|---|---|---|
| [23] | DenseNet121 | 0.572–0.842 | 0.574 –0.942 | - | - | - | - | - | - | - |
| [67] | AlexNet (S) | 0.9684 | 0.9023 | 87.99 | 92.65 | 87.94 | 90.68 | - | - | - |
| VGG16 (S) | 0.9742 | 0.9228 | 91.46 | 93.42 | 91.18 | 93.63 | - | - | - | |
| VGG19 (S) | 0.9757 | 0.9161 | 88.86 | 94.49 | 88.90 | 94.46 | - | - | - | |
| DenseNet121(S) | 0.9801 | 0.9248 | 90.01 | 95.10 | 90.00 | 95.11 | - | - | - | |
| ResNet18 (S) | 0.9766 | 0.9099 | 85.09 | 96.63 | 85.97 | 96.52 | - | - | - | |
| Inceptionv3(S) | 0.9796 | 0.9225 | 89.58 | 95.08 | 89.58 | 95.08 | - | - | - | |
| ResNet50 (S) | 0.9775 | 0.9233 | 90.59 | 94.32 | 90.43 | 94.42 | - | - | - | |
| [7] | VDSNet | 0.73 | 0.68 | - | - | - | - | 0.74 | 0.63 | 0.69 |
| [10] | DualCheXNet | - | - | - | - | - | - | 0.823 | - | - |
| [2] | CNN | 0.9240 | - | - | - | - | - | - | - | - |
| BPNN | 0.8004 | - | - | - | - | - | - | - | - | |
| CpNN | 0.8957 | - | - | - | - | - | - | - | - | |
| [8] | U-Net | 0.94 | - | - | - | - | - | - | - | - |
| [47] | Ensemble CNN | 0.940 | ||||||||
| [51] | STN based CNN | 0.96 | 0.651 | - | - | - | - | - | 0.60 | 0.70 |
| [46] | Ensemble with AlexNet | 0.862 | 0.925 | |||||||
| [26] | FHRNet | - | - | - | - | - | - | 0.812 | - | - |
| [20] | AG-CNN | - | - | - | - | - | - | 0.871 | - | - |
| [28] | Ensemble DCNN (for Cardiomegaly) | 0.93 | - | 92.00 | 94.00 | - | - | 0.97 | - | - |
| Ensemble DCNN (for Tuberculosis) | 0.90 | - | 92.00 | 88.00 | - | - | 0.94 | - | - | |
| [41] | MA-DCNN | - | - | - | - | - | - | 0.794 | - | - |
| [5] | ChestNet | 0.932 | - | - | 97.13 | 85.85 | - | 0.984 | - | - |
| [46] CNN | Maryland (MC) dataset | 0.79 | - | - | - | - | - | 0.811 | - | - |
| Shenzhen (SZ) | 0.844 | - | - | - | - | - | 0.900 | - | - | |
| Combined (CB) | 0.862 | - | - | - | - | - | 0.925 | - | - | |
| [61] | Kaggle PSNA | - | 0.755 | - | - | - | - | - | 0.793 | 0.758 |
| [45] | DarkCovidNet | 0.981 | 94.17 | 99.61 | 90.65 | - | - | - | - | 0.979 |
| [13] | GoogleNet (for normal class) | - | - | 91.00 | 91.00 | - | - | 0.964 | - | - |
| [4] | DecafCNN [78] | - | - | 78.00 | 84.00 | - | - | 87.00 | - | - |
| [35] | Ensemble DCNN | - | - | - | - | - | - | 0.99 | - | - |
| [31] | ResNet101 | 0.989 | 98.15 | 98.66 | 98.93 | - | - | - | - | 0.964 |
Table 2.5 Models with hardware used and time required for training.
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