TY - GEN
T1 - Automatic Diagnosis of Early-Stage Oral Cancer and Precancerous Lesions from ALA-PDD Images Using GAN and CNN
AU - Fujimoto, Taro
AU - Fukuzawa, Eiji
AU - Tatehara, Seiko
AU - Satomura, Kazuhito
AU - Ohya, Jun
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - A screening system for early-stage oral cancer and precancerous lesions should be established because it is difficult to detect them even for specialists and they are often detected too late. In this paper, we propose a method for automatically classifying fluorescence images acquired by ALA-PDD (Photodynamic Diagnosis using 5-Aminolevulinic Acid) into three classes: Normal, Low-Risk, High-Risk. We augment a small image dataset by training GAN (Generative adversarial networks) with Differentiable Augmentation, and then train CNN (Convolutional Neural Network) for the classification by the augmented dataset. Experimental results show good classification results, which suggest that the combination of ALA-PDD and CNN classification is a promising method for oral cancer screening. Clinical Relevance-The method proposed in this paper has a potential to be used as a screening method for early-stage oral cancer and precancerous lesions, that is non-invasive, accurate, easy to use, and does not require specialization.
AB - A screening system for early-stage oral cancer and precancerous lesions should be established because it is difficult to detect them even for specialists and they are often detected too late. In this paper, we propose a method for automatically classifying fluorescence images acquired by ALA-PDD (Photodynamic Diagnosis using 5-Aminolevulinic Acid) into three classes: Normal, Low-Risk, High-Risk. We augment a small image dataset by training GAN (Generative adversarial networks) with Differentiable Augmentation, and then train CNN (Convolutional Neural Network) for the classification by the augmented dataset. Experimental results show good classification results, which suggest that the combination of ALA-PDD and CNN classification is a promising method for oral cancer screening. Clinical Relevance-The method proposed in this paper has a potential to be used as a screening method for early-stage oral cancer and precancerous lesions, that is non-invasive, accurate, easy to use, and does not require specialization.
UR - http://www.scopus.com/inward/record.url?scp=85138127839&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85138127839&partnerID=8YFLogxK
U2 - 10.1109/EMBC48229.2022.9871868
DO - 10.1109/EMBC48229.2022.9871868
M3 - Conference contribution
C2 - 36086272
AN - SCOPUS:85138127839
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 2161
EP - 2164
BT - 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022
Y2 - 11 July 2022 through 15 July 2022
ER -
