#5557. Melanoma classification using light-Fields with morlet scattering transform and CNN: Surface depth as a valuable tool to increase detection rate

July 2026publication date
Proposal available till 21-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Radiology, Nuclear Medicine and Imaging;
Health Informatics;
Computer Graphics and Computer-Aided Design;
Radiological and Ultrasound Technology;
Computer Vision and Pattern Recognition;
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Abstract:
Medical image classification through learning-based approaches has been increasingly used, namely in the discrimination of melanoma. However, for skin lesion classification in general, such methods commonly rely on dermoscopic or other 2D-macro RGB images. This work proposes to exploit beyond conventional 2D image characteristics, by considering a third dimension (depth) that characterises the skin surface rugosity, which can be obtained from light-field images, such as those available in the SKINL2 dataset. To achieve this goal, a processing pipeline was deployed using a morlet scattering transform and a CNN model, allowing to perform a comparison between using 2D information, only 3D information, or both.
Keywords:
Classification; Light-fields; Skin lesion; Wavelet scattering

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