Classifying Skin Cancer in Digital Images Using Convolutional Neural Network with Augmentation
Skin cancer is a hazardous disease that can induces death if it is not taken care of immediately. The disease is hard to identified since the symptoms have similarities with other disease. An automatically classification system of skin cancer has been developed, but it still produced low accuracy. We use Convolutional Neural Network to enhance the accuracy of the classification. There are 2 main scenarios conducted in this research using HAM10000 dataset which has 7 classes. We compared ResNet and VGGNet architectures and obtained ResNet50 with augmentation as the best model with the accuracy of 99% and 99% macro avg.
Djuanda Adhi, Hamzah Mochtar, Aisah Siti, “Morfologi Dan Cara Membuat Diagnosis; Rata IGA. Tumor Kulit”, in Buku Ilmu Penyakit Kulit dan Kelamin. Edisi ke-IV.Jakarta, 2005, Badan Penerbit Fakultas Kedokteran Universitas Indonesia.
Buljan Marija, Bulana Vedrana, and Sandra Stanic. “Variation in Clinical Presentation of Basal Cell Carcinoma”, in University Department of Dermatology and Venereology Zagreb Croatia, 2008.
W. Howard, A. Martin, R. Steven, "Incidence Estimate of Nonmelanoma Skin Cancer (Keratinocyte Carcinomas) in the US Population, 2012", in Journal of the American Medical Association, 2015.
Skin Cancer Foundation, Skin Cancer Facts and Statistics, 2019.
E. Nasr-Esfahani et al., "Melanoma detection by analysis of clinical images using convolutional neural network," in 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2016.
Li Y, Shen L., “Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network Sensors”, in Abbreviated Name of Conf., City of Conf., Abbrev. State, 2018.
Mobiny A, Singh A, Van Nguyen H., “Risk-Aware Machine Learning Classifier for Skin Lesion Diagnosis” in J Clin Med., 2019.
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg and Li Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge” in IJCV, 2015.
He, Kaiming; Zhang, Xiangyu; Ren, Shaoqing; Sun, Jian. "Deep Residual Learning for Image Recognition", in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
A. Mikołajczyk and M. Grochowski, "Data augmentation for improving deep learning in image classification problem," in International Interdisciplinary PhD Workshop (IIPhDW), 2018.
Wen Zu, Nancy Zheng, Ning Wang, "Sensitivity, specificity, accuracy, associated confidence interval and ROC analysis with practical SAS implementations", in NESUG proceedings: health care and life sciences, 2010.
Russakovsky, Olga & Deng, Jia & Su, Hao & Krause, Jonathan & Satheesh, Sanjeev & Ma, Sean & Huang, Zhiheng & Karpathy, Andrej & Khosla, Aditya & Bernstein, Michael & Berg, Alexander & Li, Fei Fei..” ImageNet Large Scale Visual Recognition Challenge” in International Journal of Computer Vision. 2014.
Copyright (c) 2020 Zeyhan Aliyah
This work is licensed under a Creative Commons Attribution 4.0 International License.
- Manuscript submitted to IndoJC has to be an original work of the author(s), contains no element of plagiarism, and has never been published or is not being considered for publication in other journals.
- Copyright on any article is retained by the author(s). Regarding copyright transfers please see below.
- Authors grant IndoJC a license to publish the article and identify itself as the original publisher.
- Authors grant IndoJC commercial rights to produce hardcopy volumes of the journal for sale to libraries and individuals.
- Authors grant any third party the right to use the article freely as long as its original authors and citation details are identified.
- The article and any associated published material is distributed under the Creative Commons Attribution 4.0License