A Convolutional Neural Network for Lentigo Diagnosis

Sana Zorgui*, Siwar Chaabene, Bassem Bouaziz, Hadj Batatia, Lotfi Chaari

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Citations (Scopus)


Using Reflectance Confocal Microscopy (RCM) for lentigo diagnosis is today considered essential. Indeed, RCM allows fast data acquisition with a high spatial resolution of the skin. In this paper, we use a deep convolutional neural network (CNN) to perform RCM image classification in order to detect lentigo. The proposed method relies on an InceptionV3 architecture combined with data augmentation and transfer learning. The method is validated on RCM data and shows very efficient detection performance with more than 98% of accuracy.

Original languageEnglish
Title of host publicationThe Impact of Digital Technologies on Public Health in Developed and Developing Countries. ICOST 2020
EditorsMohamed Jmaiel, Mounir Mokhtari, Bessam Abdulrazak, Hamdi Aloulou, Slim Kallel
Number of pages11
ISBN (Electronic)9783030515171
ISBN (Print)9783030515164
Publication statusPublished - 23 Jun 2020
Event18th International Conference on Smart Homes and Health Telematics 2020 - Hammamet, Tunisia
Duration: 24 Jun 202026 Jun 2020

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


Conference18th International Conference on Smart Homes and Health Telematics 2020
Abbreviated titleICOST 2020


  • CNN classification
  • InceptionV3
  • Lentigo
  • Reflectance Confocal Microscopy

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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