A transfer learning with deep neural network approach for diabetic retinopathy classification

Diabetic retinopathy is an eye disease caused by high blood sugar and pressure which damages the blood vessels in the eye. Diabetic retinopathy is the root cause of more than 1% of the blindness worldwide. Early detection of this disease is crucial as it prevents it from progressing to a more severe level. However, the current machine learning-based approaches for detecting the severity level of diabetic retinopathy are either, i) rely on manually extracting features which makes an approach unpractical, or ii) trained on small dataset thus cannot be generalized. In this study, we propose a transfer learning-based approach for detecting the severity level of the diabetic retinopathy with high accuracy. Our model is a deep learning model based on global average pooling (GAP) technique with various pre-trained convolutional neural net- work (CNN) models. The experimental results of our approach, in which our best model achieved 82.4% quadratic weighted kappa (QWK), corroborate the ability of our model to detect the severity level of diabetic retinopathy efficiently

Pathological Evidence Exploration in Deep Retinal Image Diagnosis

Though deep learning has shown successful performance in classifying the label and severity stage of certain disease, most of them give few evidence on how to make prediction. Here, we propose to exploit the interpretability of deep learning application in medical diagnosis. Inspired by Koch's Postulates, a well-known strategy in medical research to identify the property of pathogen, we define a pathological descriptor that can be extracted from the activated neurons of a diabetic retinopathy detector. To visualize the symptom and feature encoded in this descriptor, we propose a GAN based method to synthesize pathological retinal image given the descriptor and a binary vessel segmentation. Besides, with this descriptor, we can arbitrarily manipulate the position and quantity of lesions. As verified by a panel of 5 licensed ophthalmologists, our synthesized images carry the symptoms that are directly related to diabetic retinopathy diagnosis. The panel survey also shows that our generated images is both qualitatively and quantitatively superior to existing methods.Comment: to appear in AAAI (2019). The first two authors contributed equally to the paper. Corresponding Author: Feng L

A deep learning approach to detect diabetic retinopathy in fundus images.

Background: Diabetic retinopathy is a disease caused due by complications of diabetes mellitus which can lead to blindness. About 33% of the US population with diabetes also show symptoms for diabetes retinopathy. If not treated, diabetic retinopathy worsens over time by progressing through two main pathological stages of non-proliferative and proliferative and four clinical stages. While the diagnostic accuracy of detecting diabetic retinopathy through machine learning have shown to be successful for OCT images, the accuracy of ultra-widefield fundus images have yet to be fully reported. This paper describes a method to non-invasively detect and diagnose diabetic retinopathy from ultra-widefield fundus images. Methods: A total of 62 graded-images were obtained from the Cleveland Clinic. A deep learning algorithm was developed to identify and extract features from the images. The algorithm was then simulated to classify the test images into one of three clinical classes. Data was collected on the accuracy and probability of the diagnosis/classification. Results: The classification algorithm had an average accuracy that ranged from 92% to 97% for the training images and 50% for the test images. Confusion matrices were created to obtain statistical measures of performance such as sensitivity, false negative rate, precision, and the false discovery rate. The sensitivity decreased from 70% to 50% as the image size increased. The precision also decreased from 65% to 50% as the image size increased. Validation methods such as image normalization and transfer learning showed no improvement in classification accuracy. Conclusion: This study demonstrates the potential for applying deep learning algorithms to classify ultra-widefield images. This study also demonstrates the need for doctors to further examine the diagnosis to account for false positives and/or misdiagnosis. Additionally, limitations and their impact on the simulation of the deep learning algorithm were explored

Automatic Classification of Bright Retinal Lesions via Deep Network Features

The diabetic retinopathy is timely diagonalized through color eye fundus images by experienced ophthalmologists, in order to recognize potential retinal features and identify early-blindness cases. In this paper, it is proposed to extract deep features from the last fully-connected layer of, four different, pre-trained convolutional neural networks. These features are then feeded into a non-linear classifier to discriminate three-class diabetic cases, i.e., normal, exudates, and drusen. Averaged across 1113 color retinal images collected from six publicly available annotated datasets, the deep features approach perform better than the classical bag-of-words approach. The proposed approaches have an average accuracy between 91.23% and 92.00% with more than 13% improvement over the traditional state of art methods.Comment: Preprint submitted to Journal of Medical Imaging | SPIE (Tue, Jul 28, 2017