ArticleJournal of clinical medicine2022
Multi-Class CNN for Classification of Multispectral and Autofluorescence Skin Lesion Clinical Images.
Article in Journal of clinical medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Multispectral imaging and autofluorescence photobleaching combined in a multi-head neural network for skin cancer classification.Frontiers in medicine · 2026Article
- Training State-of-the-Art Deep Learning Algorithms with Visible and Extended Near-Infrared Multispectral Images of Skin Lesions for the Improvement of Skin Cancer Diagnosis.Diagnostics (Basel, Switzerland) · 2025Article
- Counteracting Data Bias and Class Imbalance-Towards a Useful and Reliable Retinal Disease Recognition System.Diagnostics (Basel, Switzerland) · 2023Article
- Multispectral Imaging for Skin Diseases Assessment-State of the Art and Perspectives.Sensors (Basel, Switzerland) · 2023Review
- Multivariate Curve Resolution Alternating Least Squares Analysis of In Vivo Skin Raman Spectra.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
In this work, we propose to use an artificial neural network to classify limited data of clinical multispectral and autofluorescence images of skin lesions. Although the amount of data is limited, the deep convolutional neural network classification of skin lesions using a multi-modal image set is studied and proposed for the first time. The unique dataset consists of spectral reflectance images acquired under 526 nm, 663 nm, 964 nm, and autofluorescence images under 405 nm LED excitation. The augmentation algorithm was applied for multi-modal clinical images of different skin lesion groups to expand the training datasets. It was concluded from saliency maps that the classification performed by the convolutional neural network is based on the distribution of the major skin chromophores and endogenous fluorophores. The resulting classification confusion matrices, as well as the performance of trained neural networks, have been investigated and discussed.
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Registered trials
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