Pixel-level classification of pigmented skin cancer lesions using multispectral autofluorescence lifetime dermoscopy imaging Article Swipe
Priyanka Vasanthakumari
,
Renan Arnon Romano
,
Ramon Gabriel Teixeira Rosa
,
Ana Gabriela Sálvio
,
Vladislav V. Yakovlev
,
Cristina Kurachi
,
Jason M. Hirshburg
,
Javier A. Jo
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1364/boe.523831
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1364/boe.523831
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1364/boe.523831
- OA Status
- gold
- Cited By
- 2
- References
- 70
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400228142
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4400228142Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1364/boe.523831Digital Object Identifier
- Title
-
Pixel-level classification of pigmented skin cancer lesions using multispectral autofluorescence lifetime dermoscopy imagingWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-07-02Full publication date if available
- Authors
-
Priyanka Vasanthakumari, Renan Arnon Romano, Ramon Gabriel Teixeira Rosa, Ana Gabriela Sálvio, Vladislav V. Yakovlev, Cristina Kurachi, Jason M. Hirshburg, Javier A. JoList of authors in order
- Landing page
-
https://doi.org/10.1364/boe.523831Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1364/boe.523831Direct OA link when available
- Concepts
-
Multispectral image, Autofluorescence, Random forest, Artificial intelligence, Skin cancer, Medicine, Biopsy, Pattern recognition (psychology), Computer science, Pathology, Cancer, Internal medicine, Physics, Fluorescence, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1Per-year citation counts (last 5 years)
- References (count)
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70Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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