861 research outputs found
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
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
Deep learning for diabetic retinopathy detection and classification based on fundus images: A review.
Diabetic Retinopathy is a retina disease caused by diabetes mellitus and it is the leading cause of blindness globally. Early detection and treatment are necessary in order to delay or avoid vision deterioration and vision loss. To that end, many artificial-intelligence-powered methods have been proposed by the research community for the detection and classification of diabetic retinopathy on fundus retina images. This review article provides a thorough analysis of the use of deep learning methods at the various steps of the diabetic retinopathy detection pipeline based on fundus images. We discuss several aspects of that pipeline, ranging from the datasets that are widely used by the research community, the preprocessing techniques employed and how these accelerate and improve the models' performance, to the development of such deep learning models for the diagnosis and grading of the disease as well as the localization of the disease's lesions. We also discuss certain models that have been applied in real clinical settings. Finally, we conclude with some important insights and provide future research directions
Automated Smartphone based System for Diagnosis of Diabetic Retinopathy
Early diagnosis of diabetic retinopathy for treatment of the disease has been
failing to reach diabetic people living in rural areas. Shortage of trained
ophthalmologists, limited availability of healthcare centers, and expensiveness
of diagnostic equipment are among the reasons. Although many deep
learning-based automatic diagnosis of diabetic retinopathy techniques have been
implemented in the literature, these methods still fail to provide a
point-of-care diagnosis. This raises the need for an independent diagnostic of
diabetic retinopathy that can be used by a non-expert. Recently the usage of
smartphones has been increasing across the world. Automated diagnoses of
diabetic retinopathy can be deployed on smartphones in order to provide an
instant diagnosis to diabetic people residing in remote areas. In this paper,
inception based convolutional neural network and binary decision tree-based
ensemble of classifiers have been proposed and implemented to detect and
classify diabetic retinopathy. The proposed method was further imported into a
smartphone application for mobile-based classification, which provides an
offline and automatic system for diagnosis of diabetic retinopathy.Comment: 12 pages, 4 figures, 4 tables, 1 appendix. Copyright \copyright 2019,
IEEE. Published in: 2019 International Conference on Computing,
Communication, and Intelligent Systems (ICCCIS
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