Volume 11 - Volume 11
Detection of Skin Cancer using Inception V3 And Inception V4 Convolutional Neural Network (CNN) For Accuracy Improvement
Abstract
im: 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.
Paper Details
PaperID: 2174
Author's Name: Bhimanadhula Nandini and Dr.R. Puviarasi
Volume: Volume 11
Issues: Volume 11
Keywords: Innovative Detection, Convolutional Neural Network (CNN), Inception V3, Inception V4 CNN, Accuracy, Deep Learning.
Year: 2021
Month: June
Pages: 1138-1148