TY - GEN

T1 - Identifying Melanoma in Lesion Images Using Cycle-Consistent Adversarial Networks-Based Data Augmentation

AU - Tao, Mengjun

AU - Yan, Youwei

PY - 2021/8/15

Y1 - 2021/8/15

N2 - 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.

AB - 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.

KW - Cycle-consistent adversarial networks

KW - Data augmentation

KW - Deep convolutional neural network

KW - Images classification

KW - Melanoma

U2 - 10.1007/978-981-16-3880-0_3

DO - 10.1007/978-981-16-3880-0_3

M3 - Conference contribution/Paper

SN - 9789811638794

VL - 784

T3 - Lecture Notes in Electrical Engineering

SP - 21

EP - 28

BT - Proceedings of 2021 International Conference on Medical Imaging and Computer-Aided Diagnosis, MICAD 2021 - Medical Imaging and Computer-Aided Diagnosis

A2 - Su, Ruidan

A2 - Zhang, Yu-Dong

A2 - Liu, Han

PB - Springer

CY - Singapore

ER -