An Innovative Analysis of predicting Melanoma Skin Cancer using MobileNet and Convolutional Neural Network Algorithm

Abstract

The primary goal of this study is to propose and compare an automatic melanoma cancer detection system based on mobilenet architecture algorithm and convolutional neural network algorithm (CNN) to detect melanoma cancer. With a sample size of 10, Group 1 was the MobileNet architecture, and Group 2 was the Convolutional Neural Network algorithm. They were iterated 20 times to predict the accuracy percentage of melanoma cancer detection. The accuracy of the Mobilenet architecture algorithm (75%) is significantly higher than that of the Convolutional Neural Network (60%). The mobilenet architecture algorithm has a high statistical significance (p0.05 Independent Sample T-test). Within the scope of this study, the Mobilenet architecture algorithm outperforms Convolutional Neural networks in melanoma skin cancer detection.