Deep Convolution Neural Networks (CNN) enable advanced methods to predict the skin cancer classes through the automatic analysis of digital dermoscopic images. However, small datasets' availability often allows the models to be characterized by low prediction accuracy and poor generalization ability, which significantly influences clinical decisions. This paper proposes to use an original ensembling of multiple CNNs as feature extractors able to detect and measure skin lesions atypical criteria according to the well-known diagnostic method 7-Point Check List. The experimental results show that the Artificial Intelligence-based model can suitably manage the classification uncertainty of the single CNNs and finally distinguish melanomas from benignant nevi. Diagnostic performance is promising in terms of sensitivity and specificity towards a decision-supporting system used by a dermatologist with low experience during clinical practice.

Ensembling CNNs for dermoscopic analysis of suspicious skin lesions

Ferro M.;Sommella P.;Carratù M.;Di Leo G.;Lundgren J.;
2021-01-01

Abstract

Deep Convolution Neural Networks (CNN) enable advanced methods to predict the skin cancer classes through the automatic analysis of digital dermoscopic images. However, small datasets' availability often allows the models to be characterized by low prediction accuracy and poor generalization ability, which significantly influences clinical decisions. This paper proposes to use an original ensembling of multiple CNNs as feature extractors able to detect and measure skin lesions atypical criteria according to the well-known diagnostic method 7-Point Check List. The experimental results show that the Artificial Intelligence-based model can suitably manage the classification uncertainty of the single CNNs and finally distinguish melanomas from benignant nevi. Diagnostic performance is promising in terms of sensitivity and specificity towards a decision-supporting system used by a dermatologist with low experience during clinical practice.
2021
978-1-6654-1914-7
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4769779
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