2,548 research outputs found
A Novel Machine Learning Model Based on Exudate Localization to Detect Diabetic Macular Edema
Diabetic macular edema is one of the leading causes of legal blindness worldwide. Early, and accessible, detection of ophthalmological diseases is especially important in developing countries, where there are major limitations to access to specialized medical diagnosis and treatment. Deep learning models, such as deep convolutional neural networks have shown great success in different computer vision tasks. In medical images they have been also applied with great success. The present paper presents a novel strategy based on convolutional neural networks to combine exudates localization and eye fundus images for automatic classification of diabetic macular edema as a support for diabetic retinopathy diagnosis
Lesion detection and Grading of Diabetic Retinopathy via Two-stages Deep Convolutional Neural Networks
We propose an automatic diabetic retinopathy (DR) analysis algorithm based on
two-stages deep convolutional neural networks (DCNN). Compared to existing
DCNN-based DR detection methods, the proposed algorithm have the following
advantages: (1) Our method can point out the location and type of lesions in
the fundus images, as well as giving the severity grades of DR. Moreover, since
retina lesions and DR severity appear with different scales in fundus images,
the integration of both local and global networks learn more complete and
specific features for DR analysis. (2) By introducing imbalanced weighting map,
more attentions will be given to lesion patches for DR grading, which
significantly improve the performance of the proposed algorithm. In this study,
we label 12,206 lesion patches and re-annotate the DR grades of 23,595 fundus
images from Kaggle competition dataset. Under the guidance of clinical
ophthalmologists, the experimental results show that our local lesion detection
net achieve comparable performance with trained human observers, and the
proposed imbalanced weighted scheme also be proved to significantly improve the
capability of our DCNN-based DR grading algorithm
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
Weakly-supervised localization of diabetic retinopathy lesions in retinal fundus images
Convolutional neural networks (CNNs) show impressive performance for image
classification and detection, extending heavily to the medical image domain.
Nevertheless, medical experts are sceptical in these predictions as the
nonlinear multilayer structure resulting in a classification outcome is not
directly graspable. Recently, approaches have been shown which help the user to
understand the discriminative regions within an image which are decisive for
the CNN to conclude to a certain class. Although these approaches could help to
build trust in the CNNs predictions, they are only slightly shown to work with
medical image data which often poses a challenge as the decision for a class
relies on different lesion areas scattered around the entire image. Using the
DiaretDB1 dataset, we show that on retina images different lesion areas
fundamental for diabetic retinopathy are detected on an image level with high
accuracy, comparable or exceeding supervised methods. On lesion level, we
achieve few false positives with high sensitivity, though, the network is
solely trained on image-level labels which do not include information about
existing lesions. Classifying between diseased and healthy images, we achieve
an AUC of 0.954 on the DiaretDB1.Comment: Accepted in Proc. IEEE International Conference on Image Processing
(ICIP), 201
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