Detection of exudates in fundus photographs using convolutional neural networks

Prentašić, Pavle; Lončarić, Sven

doi:10.1109/ispa.2015.7306056

Cited by 46 publications

(36 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A DNN consisting of SAEs followed by a refined active shape model attained accurate OD segmentation. For image registration, deep learning in combination with a multi-scale Hessian matrix [136] was used to detect vessel landmarks in the retinal image, whereas convolutional neural networks have also produced excellent results in the detection of hemorrhages [137] and exudates [138] in color fundus images. It is difficult to design an automatic screening system for retinal-based diseases such as age-related molecular degeneration, diabetic retinopathy, retinoblastoma, retinal detachment, and retinitis pigmentosa, because these diseases share similar characteristics.…”

Section: Applications In Biomedicinementioning

confidence: 99%

Deep Learning and Its Applications in Biomedicine

Cao

Liu

Tan

et al. 2018

Genomics, Proteomics &Amp; Bioinformatics

497

279

View full text Add to dashboard Cite

Advances in biological and medical technologies have been providing us explosive volumes of biological and physiological data, such as medical images, electroencephalography, genomic and protein sequences. Learning from these data facilitates the understanding of human health and disease. Developed from artificial neural networks, deep learning-based algorithms show great promise in extracting features and learning patterns from complex data. The aim of this paper is to provide an overview of deep learning techniques and some of the state-of-the-art applications in the biomedical field. We first introduce the development of artificial neural network and deep learning. We then describe two main components of deep learning, i.e., deep learning architectures and model optimization. Subsequently, some examples are demonstrated for deep learning applications, including medical image classification, genomic sequence analysis, as well as protein structure classification and prediction. Finally, we offer our perspectives for the future directions in the field of deep learning.

show abstract

Section: Applications In Biomedicinementioning

confidence: 99%

Deep Learning and Its Applications in Biomedicine

Cao

Liu

Tan

et al. 2018

Genomics, Proteomics &Amp; Bioinformatics

497

279

View full text Add to dashboard Cite

show abstract

“…Deep neural networks generally require many thousands of labeled images to train effectively, but individual problems in biomedicine tend to avail neither thousands of images nor enough trained experts to label them all. Many proposed methods [1,11,14] circumvent this problem by using CNNs to perform pixel-wise binary classification. These networks take small image patches as input and output the probability of the central pixel in the patch being part of a target object.…”

Section: Deep Learning Methods For Cell Detectionmentioning

confidence: 99%

“…The output probability map is then smoothed and individual cells are detected as local probability maxima. [14] uses a CNN to detect lipid deposits in retinal images, by classify the central pixel of 65 × 65 image patches. Since these deposits are diffuse, amorphous objects, pixel-wise classification is appropriate here and there is no attempt to define the number of deposits present.…”

Section: Deep Learning Methods For Cell Detectionmentioning

confidence: 99%

Avoiding Over-Detection: Towards Combined Object Detection and Counting

Jackson

Obara

2017

Artificial Intelligence and Soft Computing

View full text Add to dashboard Cite

Further information on publisher's website:https://doi.org/10.1007/978-3-319-59063-9 7Publisher's copyright statement:The nal publication is available at Springer via https://doi.org/10.1007/978-3-319-59063-97Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-pro t purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Abstract. Existing object detection frameworks in the deep learning field generally over-detect objects, and use non-maximum suppression (NMS) to filter out excess detections, leaving one bounding box per object. This works well so long as the ground-truth bounding boxes do not overlap heavily, as would be the case with objects that partially occlude each other, or are packed densely together. In these cases it would be beneficial, and more elegant, to have a fully end-to-end system that outputs the correct number of objects without requiring a separate NMS stage. In this paper we discuss the challenges involved in solving this problem, and demonstrate preliminary results from a prototype system.

show abstract

“…In this technique, the random forest classifier was applied to classify the retinal fundus images on the basis of severity scale. Prentašić and Lončarić [16] proposed a technique to detect exudates in color retinal fundus images using convolutional neural networks. Wang et al [17] introduced a deep learning technique to understand the detection of diabetic retinopathy where they applied a regression activation map (RAM) after the pooling layer of the convolutional neural networks.…”

Section: Introductionmentioning

confidence: 99%

Fundus Image Classification Using VGG-19 Architecture with PCA and SVD

et al. 2018

View full text Add to dashboard Cite

Automated medical image analysis is an emerging field of research that identifies the disease with the help of imaging technology. Diabetic retinopathy (DR) is a retinal disease that is diagnosed in diabetic patients. Deep neural network (DNN) is widely used to classify diabetic retinopathy from fundus images collected from suspected persons. The proposed DR classification system achieves a symmetrically optimized solution through the combination of a Gaussian mixture model (GMM), visual geometry group network (VGGNet), singular value decomposition (SVD) and principle component analysis (PCA), and softmax, for region segmentation, high dimensional feature extraction, feature selection and fundus image classification, respectively. The experiments were performed using a standard KAGGLE dataset containing 35,126 images. The proposed VGG-19 DNN based DR model outperformed the AlexNet and spatial invariant feature transform (SIFT) in terms of classification accuracy and computational time. Utilization of PCA and SVD feature selection with fully connected (FC) layers demonstrated the classification accuracies of 92.21%, 98.34%, 97.96%, and 98.13% for FC7-PCA, FC7-SVD, FC8-PCA, and FC8-SVD, respectively.

show abstract

Detection of exudates in fundus photographs using convolutional neural networks

Cited by 46 publications

References 29 publications

Deep Learning and Its Applications in Biomedicine

Deep Learning and Its Applications in Biomedicine

Avoiding Over-Detection: Towards Combined Object Detection and Counting

Fundus Image Classification Using VGG-19 Architecture with PCA and SVD

Contact Info

Product

Resources

About