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

CHEE, KA CHIN and DAYANG AZRA, AWANG MAT and Abdulrazak Yahya, Saleh (2021) Skin Cancer Classification using Convolutional Neural Network with Autoregressive Integrated Moving Average. In: ICRSA 2021: 2021 4th International Conference on Robot Systems and Applications,, April 2021, Chengdu, China.

[img] PDF
neural.pdf

Download (1MB)
Official URL: https://dl.acm.org/doi/fullHtml/10.1145/3467691.34...

Abstract

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.

Item Type: Proceeding (Paper)
Uncontrolled Keywords: Autoregressive Integrated Moving Average, Classification, Convolutional Neural Network, Deep Neural Network, Skin Cancer, UNIMAS, University, Borneo, Malaysia, Sarawak, Kuching, Samarahan, IPTA, education, Universiti Malaysia Sarawak
Subjects: Q Science > Q Science (General)
Divisions: Academic Faculties, Institutes and Centres > Faculty of Cognitive Sciences and Human Development
Depositing User: Saleh Al-Hababi
Date Deposited: 13 Oct 2021 06:29
Last Modified: 13 Oct 2021 06:29
URI: http://ir.unimas.my/id/eprint/36398

Actions (For repository members only: login required)

View Item View Item