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The Effectiveness of Data Augmentation for Melanoma Skin Cancer Prediction Using Convolutional Neural Networks

Melanoma skin cancer has been a serious threat due to its high fatality. For this reason, early detection and treatments are given more attention as countermeasures. In recent years, skin cancer detection has been utilizing artificial intelligence techniques, specifically deep convolutional neural n...

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Main Authors: Kin, Wai Lee, Ka, Renee Yin Chin
Format: Conference or Workshop Item
Language:English
English
Published: IEEE Xplore 2020
Subjects:
RA Public aspects of medicine
Online Access:https://eprints.ums.edu.my/id/eprint/30132/1/The%20Effectiveness%20of%20Data%20Augmentation%20for%20Melanoma%20Skin%20Cancer%20Prediction%20Using%20Convolutional%20Neural%20Networks%20abstract.pdf
https://eprints.ums.edu.my/id/eprint/30132/2/The%20Effectiveness%20of%20Data%20Augmentation%20for%20Melanoma%20Skin%20Cancer%20Prediction%20Using%20Convolutional%20Neural%20Networks.pdf
https://eprints.ums.edu.my/id/eprint/30132/
https://ieeexplore.ieee.org/document/9257859
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spelling my.ums.eprints.301322021-07-29T01:45:55Z https://eprints.ums.edu.my/id/eprint/30132/ The Effectiveness of Data Augmentation for Melanoma Skin Cancer Prediction Using Convolutional Neural Networks Kin, Wai Lee Ka, Renee Yin Chin RA Public aspects of medicine Melanoma skin cancer has been a serious threat due to its high fatality. For this reason, early detection and treatments are given more attention as countermeasures. In recent years, skin cancer detection has been utilizing artificial intelligence techniques, specifically deep convolutional neural network. However, the performance of the convolutional neural network is highly vulnerable to different data constraints, such as the quality and quantity of the data. Therefore, this study explores the synthetization of training data using different data augmentation methods. The work presented in this paper utilizes four different categories of data augmentation methods, which include geometrical transformation, noise addition, color transformation, and image mix. Multiple layers data augmentation approach is also explored. Dataset expansion strategies and optimized dataset expansion scale are determined to improve the performance of the skin cancer classification. The core findings in our study revealed that single-layer augmentation has better performance than multiple layers augmentation approaches, where region of interest (ROI) image mix has the highest effectiveness compared to other methods. In addition, the best dataset expansion strategy is random ROI image mix. Finally, the optimized dataset expansion is determined at 300%, which yielded the best overall test accuracy at 82.9%, 4.6% improvement compared to unprocessed raw dataset. IEEE Xplore 2020 Conference or Workshop Item NonPeerReviewed text en https://eprints.ums.edu.my/id/eprint/30132/1/The%20Effectiveness%20of%20Data%20Augmentation%20for%20Melanoma%20Skin%20Cancer%20Prediction%20Using%20Convolutional%20Neural%20Networks%20abstract.pdf text en https://eprints.ums.edu.my/id/eprint/30132/2/The%20Effectiveness%20of%20Data%20Augmentation%20for%20Melanoma%20Skin%20Cancer%20Prediction%20Using%20Convolutional%20Neural%20Networks.pdf Kin, Wai Lee and Ka, Renee Yin Chin (2020) The Effectiveness of Data Augmentation for Melanoma Skin Cancer Prediction Using Convolutional Neural Networks. In: 2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), 20 November 2020, Kota Kinabalu, Malaysia. https://ieeexplore.ieee.org/document/9257859
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
English
topic RA Public aspects of medicine
spellingShingle RA Public aspects of medicine
Kin, Wai Lee
Ka, Renee Yin Chin
The Effectiveness of Data Augmentation for Melanoma Skin Cancer Prediction Using Convolutional Neural Networks
description Melanoma skin cancer has been a serious threat due to its high fatality. For this reason, early detection and treatments are given more attention as countermeasures. In recent years, skin cancer detection has been utilizing artificial intelligence techniques, specifically deep convolutional neural network. However, the performance of the convolutional neural network is highly vulnerable to different data constraints, such as the quality and quantity of the data. Therefore, this study explores the synthetization of training data using different data augmentation methods. The work presented in this paper utilizes four different categories of data augmentation methods, which include geometrical transformation, noise addition, color transformation, and image mix. Multiple layers data augmentation approach is also explored. Dataset expansion strategies and optimized dataset expansion scale are determined to improve the performance of the skin cancer classification. The core findings in our study revealed that single-layer augmentation has better performance than multiple layers augmentation approaches, where region of interest (ROI) image mix has the highest effectiveness compared to other methods. In addition, the best dataset expansion strategy is random ROI image mix. Finally, the optimized dataset expansion is determined at 300%, which yielded the best overall test accuracy at 82.9%, 4.6% improvement compared to unprocessed raw dataset.
format Conference or Workshop Item
author Kin, Wai Lee
Ka, Renee Yin Chin
author_facet Kin, Wai Lee
Ka, Renee Yin Chin
author_sort Kin, Wai Lee
title The Effectiveness of Data Augmentation for Melanoma Skin Cancer Prediction Using Convolutional Neural Networks
title_short The Effectiveness of Data Augmentation for Melanoma Skin Cancer Prediction Using Convolutional Neural Networks
title_full The Effectiveness of Data Augmentation for Melanoma Skin Cancer Prediction Using Convolutional Neural Networks
title_fullStr The Effectiveness of Data Augmentation for Melanoma Skin Cancer Prediction Using Convolutional Neural Networks
title_full_unstemmed The Effectiveness of Data Augmentation for Melanoma Skin Cancer Prediction Using Convolutional Neural Networks
title_sort effectiveness of data augmentation for melanoma skin cancer prediction using convolutional neural networks
publisher IEEE Xplore
publishDate 2020
url https://eprints.ums.edu.my/id/eprint/30132/1/The%20Effectiveness%20of%20Data%20Augmentation%20for%20Melanoma%20Skin%20Cancer%20Prediction%20Using%20Convolutional%20Neural%20Networks%20abstract.pdf
https://eprints.ums.edu.my/id/eprint/30132/2/The%20Effectiveness%20of%20Data%20Augmentation%20for%20Melanoma%20Skin%20Cancer%20Prediction%20Using%20Convolutional%20Neural%20Networks.pdf
https://eprints.ums.edu.my/id/eprint/30132/
https://ieeexplore.ieee.org/document/9257859
_version_ 1760230722847637504
score 13.211869

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