Advancement In Early Detection of Oral Cancer and Metastasis Control |
Author(s): |
| Mrs. Rimsy Dua , Thakur College of Science and Commerce; Dr. Santosh Kumar Singh, Thakur College of Science and Commerce,; Ms. Pooja Kadam, Thakur College of Science and Commerce; Ms. Shreya Patil, Thakur College of Science and Commerce |
Keywords: |
| CNN, Early Detection, MATLAB, Metastasis, Oral cancer, Random Forest, ResNet-50, SVM |
Abstract |
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This research investigates machine learning-based disability detection of oral cancer combined with metastasis management strategies. The research evaluates the diagnostic capabilities of ResNet-50 CNN, SVM, and Random Forest algorithms through assessment of image-histopathological data and patient CSV information. The dataset contains two measurement variables which include metastasis status and organ dimensions. The outcomes demonstrated that CNN(ResNet-50) scored the best identification rate of 90.08% compared to SVM 86.26% and Random Forest 74.81%. Machine learning techniques show effective potential in oral cancer diagnosis according to the obtained results that lead to early detection and better healthcare results. |
Other Details |
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Paper ID: IJSRDV13I10060 Published in: Volume : 13, Issue : 1 Publication Date: 01/04/2025 Page(s): 101-108 |
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