TY - GEN
T1 - Convolutional Neural Network Approach to Classify Skin Lesions Using Reflectance Confocal Microscopy
AU - Wodzinski, Marek
AU - Skalski, Andrzej
AU - Witkowski, Alexander
AU - Pellacani, Giovanni
AU - Ludzik, Joanna
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - We propose an approach based on a convolutional neural network to classify skin lesions using the reflectance confocal microscopy (RCM) mosaics. Skin cancers are the most common type of cancers and a correct, early diagnosis significantly lowers both morbidity and mortality. RCM is an in-vivo non-invasive screening tool that produces virtual biopsies of skin lesions but its proficient and safe use requires hard to obtain expertise. Therefore, it may be useful to have an additional tool to aid diagnosis. The proposed network is based on the ResNet architecture. The dataset consists of 429 RCM mosaics and is divided into 3 classes: melanoma, basal cell carcinoma, and benign naevi with the ground-truth confirmed by a histopathological examination. The test set classification accuracy was 87%, higher than the accuracy achieved by medical, confocal users. The results show that the proposed classification system can be a useful tool to aid in early, noninvasive melanoma detection.
AB - We propose an approach based on a convolutional neural network to classify skin lesions using the reflectance confocal microscopy (RCM) mosaics. Skin cancers are the most common type of cancers and a correct, early diagnosis significantly lowers both morbidity and mortality. RCM is an in-vivo non-invasive screening tool that produces virtual biopsies of skin lesions but its proficient and safe use requires hard to obtain expertise. Therefore, it may be useful to have an additional tool to aid diagnosis. The proposed network is based on the ResNet architecture. The dataset consists of 429 RCM mosaics and is divided into 3 classes: melanoma, basal cell carcinoma, and benign naevi with the ground-truth confirmed by a histopathological examination. The test set classification accuracy was 87%, higher than the accuracy achieved by medical, confocal users. The results show that the proposed classification system can be a useful tool to aid in early, noninvasive melanoma detection.
KW - convolutional neural network
KW - deep learning
KW - melanoma
KW - reflectance confocal microscopy
KW - skin lesion
UR - http://www.scopus.com/inward/record.url?scp=85077898023&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85077898023&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2019.8856731
DO - 10.1109/EMBC.2019.8856731
M3 - Conference contribution
C2 - 31946924
AN - SCOPUS:85077898023
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 4754
EP - 4757
BT - 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
Y2 - 23 July 2019 through 27 July 2019
ER -