A Convolutional Neural Network for Lentigo Diagnosis

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

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

Abstract

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 - 18th International Conference, ICOST 2020, Proceedings
EditorsMohamed Jmaiel, Mounir Mokhtari, Bessam Abdulrazak, Hamdi Aloulou, Slim Kallel
Pages89-99
Number of pages11
Volume12157
ISBN (Electronic)978-3-030-51517-1
DOIs
Publication statusE-pub ahead of print - 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
Volume12157
ISSN (Print)0302-9743

Conference

Conference18th International Conference on Smart Homes and Health Telematics 2020
Abbreviated titleICOST 2020
CountryTunisia
CityHammamet
Period24/06/2026/06/20

Keywords

  • CNN classification
  • InceptionV3
  • Lentigo
  • Reflectance Confocal Microscopy

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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