Automatic Identification of Diabetic Macular Edema Using a Transfer Learning-Based Approach
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http://hdl.handle.net/2183/23909
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Automatic Identification of Diabetic Macular Edema Using a Transfer Learning-Based ApproachData
2019-07-31Cita bibliográfica
Moura, J.d.; Vidal, P.L.; Novo, J.; Ortega, M. Automatic Identification of Diabetic Macular Edema Using a Transfer Learning-Based Approach. Proceedings 2019, 21, 16. https://doi.org/10.3390/proceedings2019021016
Resumo
[Abstract] 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.
Palabras chave
Computer-Aided diagnosis
Optical coherence tomography
Diabetic macular edema
Convolutional neural network
Optical coherence tomography
Diabetic macular edema
Convolutional neural network
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Atribución 4.0 Internacional (CC BY 4.0)
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2504-3900