Table 4.
Summary of feature-learning methods for GI tract disease classification
| Refs | Years | Methods | Datasets | Modality | Results |
|---|---|---|---|---|---|
| [281] | 2020 | LSST feature-based multiple diseases of the GI tract are classified by SVM, RBF Kernel. |
50 videos |
WCE |
92.23% Acc |
| [282] | 2020 | The colorectal disease is classified by ANN using CNN-based features. |
4000 frames |
VE |
93.00% F1 |
| [283] | 2020 | Multiple diseases of GI tract are classified by ANN using CNN features. | 130,00 frames | VE |
92.00% Acc |
| [284] | 2018 | FCN extracts feature for ANN-based classification of various GI tract illnesses |
20,000 frames |
VE | 78.70% Acc |
| [285] | 2018 | ANN-based classification is performed using WCNN deep features. |
10,000 frames |
VE WCE |
96% Acc |
| [286] | 2018 | Bleeding is classified by SVM using YIQ features method. |
100 frames |
WCE | 98% Acc |