A Convolutional Neural Network for Lentigo Diagnosis

16Citations
Citations of this article
30Readers
Mendeley users who have this article in their library.

This article is free to access.

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.

Cite

CITATION STYLE

APA

Zorgui, S., Chaabene, S., Bouaziz, B., Batatia, H., & Chaari, L. (2020). A Convolutional Neural Network for Lentigo Diagnosis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12157 LNCS, pp. 89–99). Springer. https://doi.org/10.1007/978-3-030-51517-1_8

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free