Jmaiel M, Mokhtari M, Abdulrazak B, Aloulou H, Kallel S. A Convolutional Neural Network for Lentigo Diagnosis.
LECTURE NOTES IN COMPUTER SCIENCE 2020. [PMCID:
PMC7313289 DOI:
10.1007/978-3-030-51517-1_8]
[Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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.
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