Training deep learning algorithms with multispectral dataset of skin lesions for the improvement of skin cancer diagnosis

Laura Rey-Barroso; Meritxell Vilaseca; Santiago Royo; Susana Puig; Josep Malvehy; Giovanni Pellacani; Ilze Lihacova; Andrey Bondarenko; Francisco J. Burgos-Fernández

doi:10.1117/12.2670926

14 February 2024 Training deep learning algorithms with multispectral dataset of skin lesions for the improvement of skin cancer diagnosis

Laura Rey-Barroso, Meritxell Vilaseca, Santiago Royo, Susana Puig, Josep Malvehy, Giovanni Pellacani, Ilze Lihacova, Andrey Bondarenko, Francisco J. Burgos-Fernández

Author Affiliations +

Proceedings Volume 12627, Translational Biophotonics: Diagnostics and Therapeutics III; 1262706 (2024) https://doi.org/10.1117/12.2670926
Event: European Conferences on Biomedical Optics, 2023, Munich, Germany

Abstract

Dermatologists are starting to make use of Computer-Aided Diagnosis based on deep learning algorithms, which can provide them with an objective judgement during evaluation of equivocal lesions. DL algorithms can be trained to classify skin lesions with datasets of diverse nature like traditional RGB, clinical and dermoscopic images, or more experimentally, with images from other modalities, such as multispectral imaging. In this work, we have evaluated and customized the different DL approaches that exist in the state of the art to classify a dataset of +500 images acquired on skin lesions. The images were acquired with a staring multispectral imaging prototype in the visible and near-infrared ranges. The best results were obtained for a customized model VGG-16 that combined 3D convolutional layers, 3D maxpooling layers and dropout regularization, leading to an overall accuracy of 71%.

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Laura Rey-Barroso, Meritxell Vilaseca, Santiago Royo, Susana Puig, Josep Malvehy, Giovanni Pellacani, Ilze Lihacova, Andrey Bondarenko, and Francisco J. Burgos-Fernández "Training deep learning algorithms with multispectral dataset of skin lesions for the improvement of skin cancer diagnosis", Proc. SPIE 12627, Translational Biophotonics: Diagnostics and Therapeutics III, 1262706 (14 February 2024); https://doi.org/10.1117/12.2670926

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KEYWORDS

Deep learning

Skin

Education and training

Skin cancer

3D modeling

Image classification

Multispectral imaging

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