Skin Cancer Classification using Convolutional Neural Network with Autoregressive Integrated Moving Average

• Main Authors: CHEE, KA CHIN, Dayang Azra, Awang Mat, Abdulrazak Yahya, Saleh
• Format: Proceeding
• Language: English
• Published: 2021
• Subjects: T Technology (General); TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://ir.unimas.my/id/eprint/36438/1/convolutional.pdf; http://ir.unimas.my/id/eprint/36438/; https://dl.acm.org/doi/abs/10.1145/3467691.3467693
• Institution: Universiti Malaysia Sarawak

Machine Learning (ML) and Deep Neural Network (DNN) based Computer-aided decision (CAD) systems show the effective implementation in solving skin cancer classification problem. However, ML approach unable to get the deep features from network flow which causes the low accuracy performance and the DNN model has the complex network with an enormous number of parameters that resulting in the limited classification accuracy. In this paper, the hybrid Convolutional Neural Network algorithm and Autoregressive Integrated Moving Average model (CNN-ARIMA) have been proposed to classify three different types of skin cancer. The proposed CNN-ARIMA able to classify skin cancer image successfully and achieved test accuracy, average sensitivity, average specificity, average precision and AUC of 96.00%, 96.02%, 97.98%, 96.13% and 0.995, respectively which outperformed the state-of-art methods.