Intelligent Deep-Learning-Enabled Decision-Making Medical System for Pancreatic Tumor Classification on CT Images
Abstract
:1. Introduction
2. Literature Review
3. The Proposed Model
3.1. Gabor Filtering Based Pre-Processing
3.2. EPO-MLT-Based Segmentation
3.3. MobileNet-Based Feature Extraction
3.4. Optimal AE-Based Classification
4. Experimental Validation
4.1. Results Analysis
4.2. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Training (%) | IDLDMS-PTC | ODL-PTNTC | WELM | KELM | ELM |
---|---|---|---|---|---|
Sensitivity | |||||
TS = 40 | 0.9995 | 0.9989 | 0.9969 | 0.9697 | 0.9679 |
TS = 50 | 0.9912 | 0.9855 | 0.9835 | 0.9823 | 0.9749 |
TS = 60 | 0.9851 | 0.9832 | 0.9720 | 0.9800 | 0.9712 |
TS = 70 | 0.9960 | 0.9759 | 0.9653 | 0.9702 | 0.9713 |
TS = 80 | 0.9958 | 0.9931 | 0.9889 | 0.9782 | 0.9628 |
Average | 0.9935 | 0.9873 | 0.9813 | 0.9761 | 0.9696 |
Specificity | |||||
TS = 40 | 0.9767 | 0.9696 | 0.9622 | 0.9687 | 0.9696 |
TS = 50 | 0.9853 | 0.9720 | 0.9684 | 0.9712 | 0.9543 |
TS = 60 | 0.9992 | 0.9882 | 0.9870 | 0.9578 | 0.9778 |
TS = 70 | 0.9959 | 0.9757 | 0.9646 | 0.9719 | 0.9531 |
TS = 80 | 0.9847 | 0.9818 | 0.9753 | 0.9764 | 0.9774 |
Average | 0.9884 | 0.9775 | 0.9715 | 0.9692 | 0.9664 |
Accuracy | |||||
TS = 40 | 0.9937 | 0.9834 | 0.9829 | 0.9544 | 0.9746 |
TS = 50 | 0.9911 | 0.9908 | 0.9760 | 0.9792 | 0.9893 |
TS = 60 | 0.9974 | 0.9850 | 0.9847 | 0.9487 | 0.9459 |
TS = 70 | 0.9876 | 0.9721 | 0.9652 | 0.9702 | 0.9432 |
TS = 80 | 0.9978 | 0.9886 | 0.9742 | 0.9837 | 0.9690 |
Average | 0.9935 | 0.9840 | 0.9766 | 0.9672 | 0.9644 |
F-score | |||||
TS = 40 | 0.9919 | 0.9892 | 0.9839 | 0.9859 | 0.9432 |
TS = 50 | 0.9940 | 0.9908 | 0.9576 | 0.9884 | 0.9651 |
TS = 60 | 0.9989 | 0.9984 | 0.9970 | 0.9710 | 0.9708 |
TS = 70 | 0.9873 | 0.9827 | 0.9671 | 0.9555 | 0.9788 |
TS = 80 | 0.9889 | 0.9798 | 0.9640 | 0.9431 | 0.9793 |
Average | 0.9948 | 0.9882 | 0.9739 | 0.9688 | 0.9674 |
No. of Folds | IDLDMS-PTC | ODL-PTNTC | WELM | KELM | ELM |
---|---|---|---|---|---|
Sensitivity | |||||
CV=6 | 0.9864 | 0.9773 | 0.9757 | 0.9607 | 0.9431 |
CV=7 | 0.9768 | 0.9761 | 0.9603 | 0.9654 | 0.9741 |
CV=8 | 0.9931 | 0.9853 | 0.9829 | 0.9445 | 0.9748 |
CV=9 | 0.9885 | 0.9670 | 0.9644 | 0.9605 | 0.9375 |
CV=10 | 0.9970 | 0.9885 | 0.9859 | 0.9546 | 0.9489 |
Average | 0.9884 | 0.9788 | 0.9738 | 0.9571 | 0.9557 |
Specificity | |||||
CV = 6 | 0.9981 | 0.9942 | 0.9927 | 0.9625 | 0.9803 |
CV = 7 | 0.9938 | 0.9880 | 0.9857 | 0.9672 | 0.9851 |
CV = 8 | 0.9967 | 0.9945 | 0.9699 | 0.9872 | 0.9937 |
CV = 9 | 0.9983 | 0.9975 | 0.9822 | 0.9965 | 0.9650 |
CV = 10 | 0.9958 | 0.9947 | 0.9788 | 0.9932 | 0.9706 |
Average | 0.9965 | 0.9938 | 0.9819 | 0.9813 | 0.9789 |
Accuracy | |||||
CV = 6 | 0.9986 | 0.9977 | 0.9846 | 0.9934 | 0.9379 |
CV = 7 | 0.9987 | 0.9944 | 0.9698 | 0.9904 | 0.9746 |
CV = 8 | 0.9807 | 0.9623 | 0.9546 | 0.9610 | 0.9422 |
CV = 9 | 0.9831 | 0.9648 | 0.9618 | 0.9486 | 0.9609 |
CV = 10 | 0.9860 | 0.9847 | 0.9715 | 0.9389 | 0.9829 |
Average | 0.9894 | 0.9808 | 0.9685 | 0.9665 | 0.9597 |
F-score | |||||
CV = 6 | 0.9861 | 0.9824 | 0.9799 | 0.9810 | 0.9657 |
CV = 7 | 0.9850 | 0.9832 | 0.9796 | 0.9744 | 0.9615 |
CV = 8 | 0.9864 | 0.9805 | 0.9784 | 0.9764 | 0.9692 |
CV = 9 | 0.9995 | 0.9992 | 0.9578 | 0.9786 | 0.9987 |
CV = 10 | 0.9948 | 0.9863 | 0.9821 | 0.9531 | 0.9600 |
Average | 0.9904 | 0.9863 | 0.9756 | 0.9727 | 0.9710 |
Methods | Sensitivity | Specificity | Accuracy |
---|---|---|---|
IDLDMS-PTC | 0.9935 | 0.9884 | 0.9935 |
ODL-PTNTC | 0.9873 | 0.9775 | 0.9840 |
WELM | 0.9776 | 0.9767 | 0.9726 |
KELM | 0.9666 | 0.9753 | 0.9669 |
ELM | 0.9627 | 0.9727 | 0.9621 |
CNN-10x10 | 0.8050 | 0.8180 | 0.8160 |
CNN-30x30 | 0.8810 | 0.8540 | 0.8590 |
CNN-50x50 | 0.9110 | 0.8650 | 0.8730 |
CNN-70x70 | 0.9150 | 0.8670 | 0.8740 |
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Vaiyapuri, T.; Dutta, A.K.; Punithavathi, I.S.H.; Duraipandy, P.; Alotaibi, S.S.; Alsolai, H.; Mohamed, A.; Mahgoub, H. Intelligent Deep-Learning-Enabled Decision-Making Medical System for Pancreatic Tumor Classification on CT Images. Healthcare 2022, 10, 677. https://doi.org/10.3390/healthcare10040677
Vaiyapuri T, Dutta AK, Punithavathi ISH, Duraipandy P, Alotaibi SS, Alsolai H, Mohamed A, Mahgoub H. Intelligent Deep-Learning-Enabled Decision-Making Medical System for Pancreatic Tumor Classification on CT Images. Healthcare. 2022; 10(4):677. https://doi.org/10.3390/healthcare10040677
Chicago/Turabian StyleVaiyapuri, Thavavel, Ashit Kumar Dutta, I. S. Hephzi Punithavathi, P. Duraipandy, Saud S. Alotaibi, Hadeel Alsolai, Abdullah Mohamed, and Hany Mahgoub. 2022. "Intelligent Deep-Learning-Enabled Decision-Making Medical System for Pancreatic Tumor Classification on CT Images" Healthcare 10, no. 4: 677. https://doi.org/10.3390/healthcare10040677