Traditional methods for early detection of melanoma rely on the visual analysis of the skin lesions performed by a dermatologist. The analysis is based on the so-called ABCDE (Asymmetry, Border irregularity, Colour variegation, Diameter, Evolution) criteria, although confirmation is obtained through biopsy performed by a pathologist. The proposed method exploits an automatic pipeline based on morphological analysis and evaluation of skin lesion dermoscopy images. Preliminary segmentation and pre-processing of dermoscopy image by SC-cellular neural networks is performed, in order to obtain ad-hoc grey-level skin lesion image that is further exploited to extract analytic innovative hand-crafted image features for oncological risks assessment. In the end, a pre-trained Levenberg-Marquardt neural network is used to perform ad-hoc clustering of such features in order to achieve an efficient nevus discrimination (benign against melanoma), as well as a numerical array to be used for follow-up rate definition and assessment. Moreover, the authors further evaluated a combination of stacked autoencoders in lieu of the Levenberg-Marquardt neural network for the clustering step.

Evaluation of Levenberg-Marquardt neural networks and stacked autoencoders clustering for skin lesion analysis, screening and follow-up

Rundo, Francesco;Ortis, Alessandro;Stanco, Filippo;Battiato, Sebastiano
2018

Abstract

Traditional methods for early detection of melanoma rely on the visual analysis of the skin lesions performed by a dermatologist. The analysis is based on the so-called ABCDE (Asymmetry, Border irregularity, Colour variegation, Diameter, Evolution) criteria, although confirmation is obtained through biopsy performed by a pathologist. The proposed method exploits an automatic pipeline based on morphological analysis and evaluation of skin lesion dermoscopy images. Preliminary segmentation and pre-processing of dermoscopy image by SC-cellular neural networks is performed, in order to obtain ad-hoc grey-level skin lesion image that is further exploited to extract analytic innovative hand-crafted image features for oncological risks assessment. In the end, a pre-trained Levenberg-Marquardt neural network is used to perform ad-hoc clustering of such features in order to achieve an efficient nevus discrimination (benign against melanoma), as well as a numerical array to be used for follow-up rate definition and assessment. Moreover, the authors further evaluated a combination of stacked autoencoders in lieu of the Levenberg-Marquardt neural network for the clustering step.
cancer; image classification; skin; image segmentation; feature extraction; neural nets; biomedical optical imaging; medical image processing; stacked autoencoders; skin lesion analysis; visual analysis; morphological analysis; skin lesion dermoscopy images; dermoscopy image; SC-cellular neural networks; ad-hoc grey-level skin lesion image; ad-hoc clustering; benign against melanoma; hand-crafted image features; Levenberg-Marquardt neural network; Software; 1707
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11769/361902
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