Detection of Skin Cancer using Inception V3 And Inception V4 Convolutional Neural Network (CNN) For Accuracy Improvement

Abstract

Aim: The main aim of this work is to measure the accuracy for automated detection of dermal cell images using the Convolutional Neural Network (CNN) algorithm. Materials and Methods: The skin images the dataset collected from International skin images collaboration (ISIC). In this framework 1200 images are used out of which (80%) are trained and (20%) are used for testing for the detection of skin cancer. 1200 images are used for group I (Inception V4) in comparison with Inception V3 and statistical analysis done using SPSS analysis. The sample size of two groups is calculated using G power with pretest power of 80 and alpha value 0.05 (error rate) with inputs 2400 (1200*2). Results: The inception V4 using CNN shows better results in mean Accuracy of 92.34±0.87 followed by inception V3 produces an accuracy of 90.34±0.13 with the significance value of <0.001. Conclusion: It is concluded that based on the execution analysis, the Inception V4 appears to be better accuracy compared with the Inception V3 algorithm.