Deep Feature Analysis in a Transfer Learning-based Approach for the Automatic Identification of Diabetic Macular Edema

Diabetic Macular Edema (DME) is one of the most common causes of vision impairment and blindness in individuals with diabetes. Among the different imaging modalities, Optical Coherence Tomography (OCT) is a non-invasive ophthalmological imaging technique that is commonly used for the diagnosis, monitoring and treatment of DME. In this context, this paper proposes a new methodology for the automatic classification of DME using OCT images. Firstly, the method extracts a set of deep features from the target OCT images using a transfer learning-based approach. Then, the most relevant subset of deep features is selected using different feature selection strategies. Finally, a machine learning approach is applied to test the potential of the implemented method. The proposed methodology was validated using an OCT image dataset retrieved from 400 different patients, being 200 with DME and 200 normal cases. The proposed system achieved satisfactory results, reaching a best accuracy of 97.50%, using only 14.65% of the deep features in the classification of this ocular pathology, demonstrating also its competitive performance with respect to others approaches of the state-of-the-art.