Implementation and Performance Analysis of Novel Support Vector Machine Classifier for Detecting Eye Cancer Image in comparison with Decision Tree
D. R. Deekshitha Varma, R. Priyanka · 2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS) · 2022
The focus of the research is to identify and detect eye cancer using novel Support Vector Machine (SVM) in contrast with Decision tree (DT). Materials and Methods: Samples are analyzed using two groups with 50 eye images. The SVM algorithm was considered as g1 and g2 as a decision tree algorithm for detection of cancerous cells in the eye image. Results: SVM has achieved a notable value of 95.0% when compared with a decision tree algorithm of 87.45% with significance (p<0.05). Conclusion: The SVM algorithm has better implication accuracy of 95% to the decision tree for the analysis and detection of eye cancer.