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).
|
(Document in English)
PDF (Author's version) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader 989kB |
Abstract
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: | |
Statistics: | download |
Deposited On: | 30 Sep 2020 09:43 |
Repository Staff Only: item control page