TY - JOUR
T1 - Deep convolutional neural network with fusion strategy for skin cancer recognition
T2 - model development and validation
AU - Juan, Chao Kuei
AU - Su, Yu Hao
AU - Wu, Chen Yi
AU - Yang, Chi Shun
AU - Hsu, Chung Hao
AU - Hung, Che Lun
AU - Chen, Yi Ju
N1 - © 2023. Springer Nature Limited.
PY - 2023/10/10
Y1 - 2023/10/10
N2 - We aimed to develop an accurate and efficient skin cancer classification system using deep-learning technology with a relatively small dataset of clinical images. We proposed a novel skin cancer classification method, SkinFLNet, which utilizes model fusion and lifelong learning technologies. The SkinFLNet's deep convolutional neural networks were trained using a dataset of 1215 clinical images of skin tumors diagnosed at Taichung and Taipei Veterans General Hospital between 2015 and 2020. The dataset comprised five categories: benign nevus, seborrheic keratosis, basal cell carcinoma, squamous cell carcinoma, and malignant melanoma. The SkinFLNet's performance was evaluated using 463 clinical images between January and December 2021. SkinFLNet achieved an overall classification accuracy of 85%, precision of 85%, recall of 82%, F-score of 82%, sensitivity of 82%, and specificity of 93%, outperforming other deep convolutional neural network models. We also compared SkinFLNet's performance with that of three board-certified dermatologists, and the average overall performance of SkinFLNet was comparable to, or even better than, the dermatologists. Our study presents an efficient skin cancer classification system utilizing model fusion and lifelong learning technologies that can be trained on a relatively small dataset. This system can potentially improve skin cancer screening accuracy in clinical practice.
AB - We aimed to develop an accurate and efficient skin cancer classification system using deep-learning technology with a relatively small dataset of clinical images. We proposed a novel skin cancer classification method, SkinFLNet, which utilizes model fusion and lifelong learning technologies. The SkinFLNet's deep convolutional neural networks were trained using a dataset of 1215 clinical images of skin tumors diagnosed at Taichung and Taipei Veterans General Hospital between 2015 and 2020. The dataset comprised five categories: benign nevus, seborrheic keratosis, basal cell carcinoma, squamous cell carcinoma, and malignant melanoma. The SkinFLNet's performance was evaluated using 463 clinical images between January and December 2021. SkinFLNet achieved an overall classification accuracy of 85%, precision of 85%, recall of 82%, F-score of 82%, sensitivity of 82%, and specificity of 93%, outperforming other deep convolutional neural network models. We also compared SkinFLNet's performance with that of three board-certified dermatologists, and the average overall performance of SkinFLNet was comparable to, or even better than, the dermatologists. Our study presents an efficient skin cancer classification system utilizing model fusion and lifelong learning technologies that can be trained on a relatively small dataset. This system can potentially improve skin cancer screening accuracy in clinical practice.
KW - Humans
KW - Skin Neoplasms/pathology
KW - Melanoma/pathology
KW - Neural Networks, Computer
KW - Skin/pathology
KW - Keratosis, Seborrheic/diagnosis
UR - http://www.scopus.com/inward/record.url?scp=85173717340&partnerID=8YFLogxK
U2 - 10.1038/s41598-023-42693-y
DO - 10.1038/s41598-023-42693-y
M3 - 文章
C2 - 37816815
AN - SCOPUS:85173717340
SN - 2045-2322
VL - 13
SP - 17087
JO - Scientific Reports
JF - Scientific Reports
IS - 1
M1 - 17087
ER -