Fig 1.
Proposed CAD system framework.
Fig 2.
Anisotropic filtering result.
Fig 3.
Proposed segmentation framework.
Fig 4.
Feature extraction & reduction framework.
Table 1.
Features and formulas.
Fig 5.
Proposed tumor detection & localization framework.
Fig 6.
Proposed 11-layer AlexNet-CNN framework for classification.
Table 2.
Proposed AlexNet-CNN 11 layers details.
Table 3.
Tools requirements and system specification.
Fig 7.
Dataset of CT images for pancreatic cancer.
Table 4.
Pancreatic tumor CTs images dataset details.
Fig 8.
Proposed GUI-based CAD system for PC Tumor.
Table 5.
Comparative results of watershed vs. U-Net segmentation in pancreatic tumor detection.
Fig 9.
System performance metrics for tumor detection.
Fig 10.
Pancreatic tumor detection results.
Fig 11.
Comparison chart of performance metrics with other models for tumor detection.
Fig 12.
Comparison graph of performance evaluation with other models for tumor detection.
Table 6.
Detection performance metrics comparison of proposed and other models.
Fig 13.
System performance metrics for tumor classification.
Fig 14.
Pancreatic tumor classification results.
Fig 15.
Comparison chart of performance metrics with other models for tumor classification.
Fig 16.
Comparison graph of performance evaluation with other models for tumor classification.
Table 7.
Classification performance metrics comparison of proposed and other models.
Fig 17.
A. Training and validation Chart of accuracy and loss. B. Training and validation Chart of accuracy and loss.
Fig 18.
ROC curve for tumor classification.
Table 8.
AlexNet-CNN model training and validation details.