2,525 research outputs found
A Decision Support Framework for Automated Screening of Diabetic Retinopathy
The early signs of diabetic retinopathy (DR) are depicted by
microaneurysms among other signs. A prompt diagnosis when the
disease is at the early stage can help prevent irreversible
damages to the diabetic eye. In this paper, we propose a decision
support system (DSS) for automated screening of early signs of
diabetic retinopathy. Classification schemes for deducing the
presence or absence of DR are developed and tested. The detection
rule is based on binary-hypothesis testing problem which
simplifies the problem to yes/no decisions. An analysis of the
performance of the Bayes optimality criteria applied to DR is also
presented. The proposed DSS is evaluated on the real-world data.
The results suggest that by biasing the classifier towards DR
detection, it is possible to make the classifier achieve good
sensitivity
Knowledge Transfer for Melanoma Screening with Deep Learning
Knowledge transfer impacts the performance of deep learning -- the state of
the art for image classification tasks, including automated melanoma screening.
Deep learning's greed for large amounts of training data poses a challenge for
medical tasks, which we can alleviate by recycling knowledge from models
trained on different tasks, in a scheme called transfer learning. Although much
of the best art on automated melanoma screening employs some form of transfer
learning, a systematic evaluation was missing. Here we investigate the presence
of transfer, from which task the transfer is sourced, and the application of
fine tuning (i.e., retraining of the deep learning model after transfer). We
also test the impact of picking deeper (and more expensive) models. Our results
favor deeper models, pre-trained over ImageNet, with fine-tuning, reaching an
AUC of 80.7% and 84.5% for the two skin-lesion datasets evaluated.Comment: 4 page
Zoom-in-Net: Deep Mining Lesions for Diabetic Retinopathy Detection
We propose a convolution neural network based algorithm for simultaneously
diagnosing diabetic retinopathy and highlighting suspicious regions. Our
contributions are two folds: 1) a network termed Zoom-in-Net which mimics the
zoom-in process of a clinician to examine the retinal images. Trained with only
image-level supervisions, Zoomin-Net can generate attention maps which
highlight suspicious regions, and predicts the disease level accurately based
on both the whole image and its high resolution suspicious patches. 2) Only
four bounding boxes generated from the automatically learned attention maps are
enough to cover 80% of the lesions labeled by an experienced ophthalmologist,
which shows good localization ability of the attention maps. By clustering
features at high response locations on the attention maps, we discover
meaningful clusters which contain potential lesions in diabetic retinopathy.
Experiments show that our algorithm outperform the state-of-the-art methods on
two datasets, EyePACS and Messidor.Comment: accepted by MICCAI 201
Detection of Hard Exudates in Retinal Fundus Images using Deep Learning
Diabetic Retinopathy (DR) is a retinal disorder that affects the people
having diabetes mellitus for a long time (20 years). DR is one of the main
reasons for the preventable blindness all over the world. If not detected early
the patient may progress to severe stages of irreversible blindness. Lack of
Ophthalmologists poses a serious problem for the growing diabetes patients. It
is advised to develop an automated DR screening system to assist the
Ophthalmologist in decision making. Hard exudates develop when DR is present.
It is important to detect hard exudates in order to detect DR in an early
stage. Research has been done to detect hard exudates using regular image
processing techniques and Machine Learning techniques. Here, a deep learning
algorithm has been presented in this paper that detects hard exudates in fundus
images of the retina.Comment: 5 Pages, 3 figures, 2 tables, International Conference on Systems,
Computation, Automation and Networking http://icscan.in
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