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

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Department of Computing, University of A Coruña, 15071 A Coruña, Spain
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CITIC—Research Center of Information and Communication Technologies, University of A Coruña, 15071 A Coruña, Spain
*
Author to whom correspondence should be addressed.
Presented at the 2nd XoveTIC Conference, A Coruña, Spain, 5–6 September 2019.
Proceedings 2019, 21(1), 16; https://doi.org/10.3390/proceedings2019021016
Published: 31 July 2019
(This article belongs to the Proceedings of XoveTIC Conference)
This paper presents a complete system for the automatic identification of pathological Diabetic Macular Edema (DME) cases using Optical Coherence Tomography (OCT) images as source of 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 Neural Network (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 train and 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.
Keywords: Computer-Aided Diagnosis; Optical Coherence Tomography; Diabetic Macular Edema; Convolutional Neural Network Computer-Aided Diagnosis; Optical Coherence Tomography; Diabetic Macular Edema; Convolutional Neural Network
MDPI and ACS Style

Moura, J.; Vidal, P.L.; Novo, J.; Ortega, M. Automatic Identification of Diabetic Macular Edema Using a Transfer Learning-Based Approach. Proceedings 2019, 21, 16.

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