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Pixel-level classification of pigmented skin cancer lesions using multispectral autofluorescence lifetime dermoscopy imaging.

Authors :
Vasanthakumari P
Romano RA
Rosa RGT
Salvio AG
Yakovlev V
Kurachi C
Hirshburg JM
Jo JA
Source :
Biomedical optics express [Biomed Opt Express] 2024 Jul 09; Vol. 15 (8), pp. 4557-4583. Date of Electronic Publication: 2024 Jul 09 (Print Publication: 2024).
Publication Year :
2024

Abstract

There is no clinical tool available to primary care physicians or dermatologists that could provide objective identification of suspicious skin cancer lesions. Multispectral autofluorescence lifetime imaging (maFLIM) dermoscopy enables label-free biochemical and metabolic imaging of skin lesions. This study investigated the use of pixel-level maFLIM dermoscopy features for objective discrimination of malignant from visually similar benign pigmented skin lesions. Clinical maFLIM dermoscopy images were acquired from 60 pigmented skin lesions before undergoing a biopsy examination. Random forest and deep neural networks classification models were explored, as they do not require explicit feature selection. Feature pools with either spectral intensity or bi-exponential maFLIM features, and a combined feature pool, were independently evaluated with each classification model. A rigorous cross-validation strategy tailored for small-size datasets was adopted to estimate classification performance. Time-resolved bi-exponential autofluorescence features were found to be critical for accurate detection of malignant pigmented skin lesions. The deep neural network model produced the best lesion-level classification, with sensitivity and specificity of 76.84%±12.49% and 78.29%±5.50%, respectively, while the random forest classifier produced sensitivity and specificity of 74.73%±14.66% and 76.83%±9.58%, respectively. Results from this study indicate that machine-learning driven maFLIM dermoscopy has the potential to assist doctors with identifying patients in real need of biopsy examination, thus facilitating early detection while reducing the rate of unnecessary biopsies.<br />Competing Interests: The authors declare no conflicts of interest.<br /> (© 2024 Optica Publishing Group.)

Details

Language :
English
ISSN :
2156-7085
Volume :
15
Issue :
8
Database :
MEDLINE
Journal :
Biomedical optics express
Publication Type :
Academic Journal
Accession number :
39346997
Full Text :
https://doi.org/10.1364/BOE.523831