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Identifying Melanoma in Lesion Images Using Cycle-Consistent Adversarial Networks-Based Data Augmentation

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Publication date15/08/2021
Host publicationProceedings of 2021 International Conference on Medical Imaging and Computer-Aided Diagnosis, MICAD 2021 - Medical Imaging and Computer-Aided Diagnosis
EditorsRuidan Su, Yu-Dong Zhang, Han Liu
Place of PublicationSingapore
Number of pages8
ISBN (print)9789811638794
<mark>Original language</mark>English

Publication series

NameLecture Notes in Electrical Engineering
Volume784 LNEE
ISSN (Print)1876-1100
ISSN (electronic)1876-1119


Early detection of melanoma is extremely important because melanoma is curable at the early stage. Due to the state-of-the-art performance of the Convolutional Neural Networks (CNNs), the CNNs have been widely used for the task. However, hand labeled data is not easily obtained in practical settings. In this paper, we firstly employ generative adversarial network (GAN) to artificially enlarge the dataset, which can generate fake data based on the generative confrontation network. Therefore, the problem of insufficient training samples in melanoma classification tasks has been alleviated. Second, CNNs is employed in our paper to automatically classification, which proved to be more effectively solve the problem of small discrimination between different categories. Based on the proposed method, the experimental results show that the use of deep learning technology can effectively improve the performance of the model in the melanoma classification task, with an average accuracy value of 94.5%, which is nearly 1.9% higher than the previous approaches.