TABLE I. Performance Evaluation Matrices for the Proposed Deep Parallel Convolutional Capsule Network With and Without N-CLAHE and Pretraining.
| Dataset | Evaluated metrics | Capsule Network with Sequential Convolutional Blocks | Capsule Network with Parallely Concatenated Convolutional Blocks |
|---|---|---|---|
| POCUSDdataset | Accuracy | 0.943 | 0.983 |
| Precision | 0.939 | 0.981 | |
| Sensitivity | 0.931 | 0.985 | |
| F1 score | 0.932 | 0.987 | |
| Specificity | 0.932 | 0.991 | |
| Dataset-1 | Accuracy | 0.945 | 0.990 |
| Precision | 0.949 | 0.992 | |
| Sensitivity | 0.951 | 0.997 | |
| F1 score | 0.950 | 0.996 | |
| Specificity | 0.951 | 0.996 | |
| Dataset-2 | Accuracy | 0.939 | 0.989 |
| Precision | 0.944 | 0.986 | |
| Sensitivity | 0.946 | 0.99 | |
| F1 score | 0.941 | 0.993 | |
| Specificity | 0.944 | 0.99 |
By applying data preprocessing techniques, significant improvement in results has been achieved. More improvement in results is achieved by applying the pretraining technique.