TABLE 4.
Accuracy metrics in terms of true positive (TP), true negative (TN), false positive (FP), false negative (FN), accuracy, specificity, sensitivity and precision
| Metrics | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Architecture | Classes | TP | TN | FP | FN | Accuracy | Specificity | Sensitivity | Precision | Total |
| Proposed CNN Architecture | Mild | 186 | 442 | 10 | 14 | 96.32% | 0.978 | 0.930 | 0.949 | 200 |
| Moderate | 181 | 448 | 14 | 9 | 96.47% | 0.970 | 0.953 | 0.928 | 190 | |
| Severe | 139 | 507 | 3 | 3 | 99.08% | 0.994 | 0.979 | 0.979 | 142 | |
| Critical | 116 | 529 | 3 | 4 | 98.93% | 0.994 | 0.967 | 0.967 | 120 |
This article is being made freely available through PubMed Central as part of the COVID-19 public health emergency response. It can be used for unrestricted research re-use and analysis in any form or by any means with acknowledgement of the original source, for the duration of the public health emergency.