TY - GEN
T1 - Classification of skin pigmented lesions based on deep residual network
AU - Qi, Yunfei
AU - Lin, Shaofu
AU - Huang, Zhisheng
PY - 2019
Y1 - 2019
N2 - There are various of skin pigmented lesions with high risk. Melanoma is one of the most dangerous forms of skin cancer. It is one of the important research directions of medical artificial intelligence to carry out classification research of skin pigmented lesions based on deep learning. It can assist doctors to make clinical diagnosis and make patients receive treatment as soon as possible to improve survival rate. Aiming at the similar and imbalanced dermoscopic image data of pigmented lesions, this paper proposes a deep residual network improved by Squeeze-and-Excitation module, and dynamic update class-weight, in batches, with model ensemble adjustment strategies to change the attention of imbalanced data. The results show that the above method can increase the average precision by 9.1%, the average recall by 15.3%, and the average F1-score by 12.2%, compared with the multi-class classification using the deep residual network. Thus, the above method is a better classification model and weight adjustment strategy.
AB - There are various of skin pigmented lesions with high risk. Melanoma is one of the most dangerous forms of skin cancer. It is one of the important research directions of medical artificial intelligence to carry out classification research of skin pigmented lesions based on deep learning. It can assist doctors to make clinical diagnosis and make patients receive treatment as soon as possible to improve survival rate. Aiming at the similar and imbalanced dermoscopic image data of pigmented lesions, this paper proposes a deep residual network improved by Squeeze-and-Excitation module, and dynamic update class-weight, in batches, with model ensemble adjustment strategies to change the attention of imbalanced data. The results show that the above method can increase the average precision by 9.1%, the average recall by 15.3%, and the average F1-score by 12.2%, compared with the multi-class classification using the deep residual network. Thus, the above method is a better classification model and weight adjustment strategy.
KW - Deep learning
KW - Imbalanced data
KW - Model ensemble
KW - Multi-classification
KW - Residual network
KW - Skin lesions
UR - http://www.scopus.com/inward/record.url?scp=85075648754&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85075648754&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-32962-4_6
DO - 10.1007/978-3-030-32962-4_6
M3 - Conference contribution
AN - SCOPUS:85075648754
SN - 9783030329617
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 58
EP - 67
BT - Health Information Science
A2 - Wang, Hua
A2 - Siuly, Siuly
A2 - Zhang, Yanchun
A2 - Zhou, Rui
A2 - Martin-Sanchez, Fernando
A2 - Huang, Zhisheng
PB - Springer
T2 - 8th International Conference on Health Information Science, HIS 2019
Y2 - 18 October 2019 through 20 October 2019
ER -