Predicting Diabetic Retinopathy in OCTA Images using a Deep Learning Framework
The demand for diabetic retinopathy (DR) screening is growing as the prevalence of diabetes mellitus (DM) rises. Optical coherence tomography angiography (OCTA) is a novel imaging technique that has shown promising results for the early diagnosis of diabetic retinopathy (DR). However, little researc...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/385009 |
| Acceso en línea: | https://hdl.handle.net/2117/385009 |
| Access Level: | acceso abierto |
| Palabra clave: | Diabetic retinopathy Deep learning (Machine learning) diabetic retinopathy OCTA deep learning machine learning artificial intelligence diabetes convolutional neural network transfer learning super-resolution optical coherence tomography angiography medical imaging automated diagnosis Retinopatia diabètica Aprenentatge profund Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
| Sumario: | The demand for diabetic retinopathy (DR) screening is growing as the prevalence of diabetes mellitus (DM) rises. Optical coherence tomography angiography (OCTA) is a novel imaging technique that has shown promising results for the early diagnosis of diabetic retinopathy (DR). However, little research exists on automated frameworks for DR detection and grading based on OCTA data. In this study, we evaluate for the first time the effectiveness of a convolutional neural network (CNN) in classifying DM, DR and referable DR (RF-DR) using the largest dataset of labelled OCTA images currently available. The dataset is composed by 3×3 mm and 6×6 mm images of the superficial and deep capillary plexuses of 726 eyes. Three preprocessing schemes were implemented where either individual or concatenations of the original images or their super-resolved equivalents were used. The InceptionV3 architecture pretrained on the ImageNet dataset was fine-tuned for robust classification. After performing three times three-fold cross-validation, the proposed CNN classifier reported mean AUC values of 0.80, 0.81 and 0.92 for DM, DR and RF-DR diagnosis respectively. These results motivate further exploration of CNN-based classification of OCTA images to assist on DR diagnosis and alleviate the burden of mass screenings. |
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