Title:
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Skin lesion classification from dermoscopic images using deep learning techniques
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Author:
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Romero-Lopez, Adrià; Giró Nieto, Xavier; Burdick, Jack; Marques, Oge
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Other authors:
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Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions; Universitat Politècnica de Catalunya. GPI - Grup de Processament d'Imatge i Vídeo |
Abstract:
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The recent emergence of deep learning methods for medical image analysis has enabled the development of intelligent medical imaging-based diagnosis systems that can assist the human expert in making better decisions about a patient’s health. In this paper we focus on the problem of skin lesion classification, particularly early melanoma detection, and present a deep-learning based approach to solve the problem of classifying a dermoscopic image containing a skin lesion as malignant or benign. The proposed solution is built around the VGGNet convolutional neural network architecture and uses the transfer learning paradigm. Experimental results are encouraging: on the ISIC Archive dataset, the proposed method achieves a sensitivity value of 78.66%, which is significantly higher than the current state of the art on that dataset. |
Subject(s):
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-Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Reconeixement de formes -Àrees temàtiques de la UPC::Ciències de la salut::Medicina::Dermatologia -Diagnostic imaging -Image processing--Digital techniques -Dermatology -medical image analysis -deep learning -machine learning -Diagnòstic per la imatge -Imatges mèdiques -Dermatologia -Imatges -- Processament -- Tècniques digitals |
Rights:
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Document type:
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Article - Submitted version Conference Object |
Published by:
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ACTA Press
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