Figures & data
Figure 1. Flow diagram of the proposed procedure.
Figure 2. BCDGU-Net.
Figure 3. Xception architecture.
Figure 4. An extreme version of inception module.
Figure 5. Concept of the Xception architecture.
Figure 6. Types of images.
Table 1. The details of the Kvasir dataset.
Table 2. Cancer size and depth-based sensitivity.
Figure 7. The results of ROC curves.
Figure 8. FROC curve image-based sensitivity.
Figure 9. FROC curve lesion-based sensitivity.
Figure 10. Proposed segmentation.
Table 3. A comparison of CNN architectures for the minimization of false positives.
Figure 11. False positive.
Table 4. Comparative segmentation performance with fuzzy C means, global thresholding, BCD-Net and U Net.
Figure 12. Graphical plot for Table .
Figure 13. Graphical plot for Table .
Table 5. Comparative chart of classification performance with Res-Net, VGG-Net and Mobile-Net.
Figure 14. Proposed AUC curve.
Figure 15. Stages of gastric cancer.
Table 6. Evaluation results of cancer segmentation.