Skin Cancer Classification using CNN in Comparison with Support Vector Machine for Better Accuracy

Abstract

Using the Convolutional Neural Network (CNN) algorithm to perform unique classification of skin cancer and evaluating the performance of the SVM approach. n this research work, skin cancer detection has been carried out using algorithms such as CNN and SVM and the accuracy was determined for the same. Two groups are statistically analyzed with the sample size 20 for both the groups, with a pretest g power of 80%. When the CNN algorithm's performance is examined, it is found that the accuracy is 95.03% for CNN and 93.04% for the SVM algorithm. The sample size will be computed using the mean, standard deviation, and standard error, as well as the independent samples test if the significance is less than one. According to the statistical data, the algorithm's accuracy (0.490), specificity (0.009), and p>0.05 significant values are all p0.05. The result shows that CNN algorithm's accuracy was better than SVM algorithm for skin cancer detection.