Explainable end-to-end deep learning for diabetic retinopathy detection across multiple datasets
Mohamed Chetoui, Moulay A. Akhloufi
Abstract
: The obtained results achieve state-of-the-art performance and outperform past published works relying on training using only publicly available datasets. The proposed approach can robustly classify fundus images and detect DR. An explainability model was developed and showed that our model was able to efficiently identify different signs of DR and detect this health issue.
Topics & Concepts
Diabetic retinopathyMedicineArtificial intelligenceDeep learningFundus (uterus)Convolutional neural networkRetinalEnd-to-end principleRetinopathyPattern recognition (psychology)Computer scienceOptometryMachine learningOphthalmologyDiabetes mellitusEndocrinologyRetinal Imaging and AnalysisRetinal Diseases and TreatmentsDigital Imaging for Blood Diseases