Automatic Identification of Diabetic Macular Edema Using a Transfer Learning-Based Approach

• Joaquim de Moura ^[1] ; Plácido L. Vidal ; Jorge Novo ^[1] ; Marcos Ortega ^[1]
  1. [1] Universidade da Coruña

     Universidade da Coruña

     A Coruña, España

     
• Localización: XoveTIC 2019: The 2nd XoveTIC Conference (XoveTIC 2019), A Coruña, Spain, 5–6 September / Alberto Alvarellos González (ed. lit.), Joaquim de Moura (ed. lit.), Beatriz Botana Barreiro (ed. lit.), Javier Pereira-Loureiro (ed. lit.), Manuel Francisco González Penedo (ed. lit.), 2019, ISBN 978-3-03921-444-0
• Idioma: inglés
• Enlaces
  • Texto Completo Libro (pdf)
• Resumen

  • This paper presents a complete system for the automatic identification of pathological Diabetic Macular Edema (DME) cases using Optical Coherence Tomography (OCT) images as sourceof information. To do so, the system extracts a set of deep features using a transfer learning-based approach from different fully-connected layers and different pre-trained Convolutional NeuralNetwork (CNN) models. Next, the most relevant subset of deep features is identified using representative feature selection methods. Finally, a machine learning strategy is applied to trainand test the potential of the identified deep features in the pathological classification process. Satisfactory results were obtained, demonstrating the suitability of the presented system to filter those pathological DME cases, helping the specialist to optimize their diagnostic procedures.