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Lentigo detection using a deep learning approach

Zorgui, Sana and Chaabene, Siwar and Batatia, Hadj and Chaari, Lotfi Lentigo detection using a deep learning approach. (2020) In: International Conference On Smart Living and Public Health (ICOST 2020), 24 June 2020 - 26 June 2020 (Hammamet, Tunisia).

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Reflectance confocal microscopy (RCM) allows fast data acquisition with a high resolution of the skin. In fact, RCM images are becoming more and more used for lentigo diagnosis. In this paper, we propose a new classification method to automate specific steps in lentigo diagnosis. Our method is based on a convolutional neural network (CNN) on InceptionV3 architecture combined with data augmentation and transfer learning. The experimental validation showed the efficiency of our model by reaching an accuracy of 98.14%.

Item Type:Conference or Workshop Item (Paper)
HAL Id:hal-02968415
Audience (conference):International conference proceedings
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Other partners > Université de Sfax (TUNISIA)
Laboratory name:
Deposited On:30 Sep 2020 09:43

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