Selected article for: "convolutional neural network and image classification"
Author: Zorgui, Sana; Chaabene, Siwar; Bouaziz, Bassem; Batatia, Hadj; Chaari, Lotfi
Title: A Convolutional Neural Network for Lentigo Diagnosis
  • Cord-id: zfcsw281
  • Document date: 2020_5_31
    • ID: zfcsw281
    Snippet: 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 perfor
    Document: 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.

    Search related documents:
    Co phrase search for related documents
    • accuracy increase and magnetic resonance: 1, 2
    • accuracy value and magnetic resonance: 1