Automated Detection of Diabetic Retinopathy using Deep Learning
By Carson K. Lam, Darvin Yi, Margaret Guo, Tony Lindsey. AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2018
357 citations recorded by the source corpus.
Diabetic retinopathy is a leading cause of blindness among working-age adults. Early detection of this condition is critical for good prognosis. In this paper, we demonstrate the use of convolutional neural networks (CNNs) on color fundus images for the recognition task of diabetic retinopathy staging. Our network models achieved test metric performance comparable to baseline literature results, with validation sensitivity of 95%. We additionally explored multinomial classification models, and demonstrate that errors primarily occur in the misclassification of mild disease as normal due to the CNNs inability to detect subtle disease features. We discovered that preprocessing with contrast limited adaptive histogram equalization and ensuring dataset fidelity by expert verification of class labels improves recognition of subtle features. Transfer learning on pretrained GoogLeNet and AlexNet models from ImageNet improved peak test set accuracies to 74.5%, 68.8%, and 57.2% on 2-ary, 3-ary, and 4-ary classification models, respectively.
View Paper: https://www.semanticscholar.org/paper/e9fbe155954c01560d8e2504741692a40e3ffa31
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Citations in the corpus
- IDRiD: Diabetic Retinopathy - Segmentation and Grading Challenge. (2020, methodology): “Whereas some approaches initialized the parameters with those of pre-trained models (on non-medical images), then “fine-tuned” ( Tajbakhsh et al., 2016 ) the network parameters for DR screening ( Gulshan et al., 2016; Carson Lam et al., 2018 ).”
- Comparison of Conventional Statistical Methods with Machine Learning in Medicine: Diagnosis, Drug Development, and Treatment (2020, background): “…AI companies are being increasingly involved in the area of medicine, from diagnosis to treatment and For instance, convolutional neural networks (used in image recognition and processing) have been able to e ff ectively improve the diagnostic process of diabetic retinopathy [3,4]. drug development.”