Training Deep Convolutional Neural Networks with Active Learning for Exudate Classification in Eye Fundus Images

Otálora, Sebastian; Perdomo, Oscar J.; González, Fabio A.; Müller, Henning

doi:10.1007/978-3-319-67534-3_16

Cited by 17 publications

(10 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This model is then queried to find the most informative samples (Settles, 2009). Already used as a tool to build datasets in machine learning, this technique is now used with deep neural networks, including for the annotation of medical images (Gorriz et al, 2017; Otálora et al, 2017) or in remote-sensing (Liu P. et al, 2017). Another way to reduce the annotation effort would be to share the annotated data.…”

Section: Discussionmentioning

confidence: 99%

Convolutional Neural Networks for the Automatic Identification of Plant Diseases

et al. 2019

View full text Add to dashboard Cite

Deep learning techniques, and in particular Convolutional Neural Networks (CNNs), have led to significant progress in image processing. Since 2016, many applications for the automatic identification of crop diseases have been developed. These applications could serve as a basis for the development of expertise assistance or automatic screening tools. Such tools could contribute to more sustainable agricultural practices and greater food production security. To assess the potential of these networks for such applications, we survey 19 studies that relied on CNNs to automatically identify crop diseases. We describe their profiles, their main implementation aspects and their performance. Our survey allows us to identify the major issues and shortcomings of works in this research area. We also provide guidelines to improve the use of CNNs in operational contexts as well as some directions for future research.

show abstract

Section: Discussionmentioning

confidence: 99%

Convolutional Neural Networks for the Automatic Identification of Plant Diseases

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Oktalora et al [16] classify for exudate. Exudate is a symptom in the form of a yellow spot, irregular shape, arising from lipid infiltration in the retina.…”

Section: Telkomnika Telecommun Comput El Controlmentioning

confidence: 99%

“…The size of data is 48x48 pixels. The classification consists of two categories: normal and exudate [16]. Sadek et al build transfer learning to classify 3 categories include normal, exudates, and drusen.…”

Section: Telkomnika Telecommun Comput El Controlmentioning

confidence: 99%

Transfer learning with multiple pre-trained network for fundus classification

2020

View full text Add to dashboard Cite

Transfer learning (TL) is a technique of reuse and modify a pre-trained network. It reuses feature extraction layer at a pre-trained network. A target domain in TL obtains the features knowledge from the source domain. TL modified classification layer at a pre-trained network. The target domain can do new tasks according to a purpose. In this article, the target domain is fundus image classification includes normal and neovascularization. Data consist of 100 patches. The comparison of training and validation data was 70:30. The selection of training and validation data is done randomly. Steps of TL i.e load pre-trained networks, replace final layers, train the network, and assess network accuracy. First, the pre-trained network is a layer configuration of the convolutional neural network architecture. Pre-trained network used are AlexNet, VGG16, VGG19, ResNet50, ResNet101, GoogLeNet, Inception-V3, InceptionResNetV2, and squeezenet. Second, replace the final layer is to replace the last three layers. They are fully connected layer, softmax, and output layer. The layer is replaced with a fully connected layer that classifies according to number of classes. Furthermore, it's followed by a softmax and output layer that matches with the target domain. Third, we trained the network. Networks were trained to produce optimal accuracy. In this section, we use gradient descent algorithm optimization. Fourth, assess network accuracy. The experiment results show a testing accuracy between 80% and 100%.

show abstract

“…This procedure is pursued for all layers until desirable results are achieved [135][136][137][138][139][140][141][142]. [145] CNN model Detection of exudates -- [113] Multiscale and CNN Detection of fovea and OD -AC: 97%…”

Section: Latest Trendsmentioning

confidence: 99%

“…Back propagation and gradient descent methods are commonly applied to train CNNs and to make them faster at learning and convergence. The main demerit of CNNs is that it needs a large amount of memory to store the results of the convolutional layer that finally forwards to the back propagation layer to compute gradients [143][144][145][146][147][148][149]. Some of the other variants of CNN networks, like Alexnet (https://github.com/BVLC/caffe/tree/master/models/bvlcalexnet), Lenet (http: //deeplearning.net/tutorial/lenet.html), faster R-CNN (https://github.com/ShaoqingRen/fasterrcnn), googlenet (https://github.com/BVLC/caffe/tree/master/models/bvlcgooglenet), Resnet (https://github.…”

Section: Latest Trendsmentioning

confidence: 99%

Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

Qureshi

Abbas

2019

Symmetry

View full text Add to dashboard Cite

Diabetic retinopathy (DR) is a complication of diabetes that exists throughout the world. DR occurs due to a high ratio of glucose in the blood, which causes alterations in the retinal microvasculature. Without preemptive symptoms of DR, it leads to complete vision loss. However, early screening through computer-assisted diagnosis (CAD) tools and proper treatment have the ability to control the prevalence of DR. Manual inspection of morphological changes in retinal anatomic parts are tedious and challenging tasks. Therefore, many CAD systems were developed in the past to assist ophthalmologists for observing inter- and intra-variations. In this paper, a recent review of state-of-the-art CAD systems for diagnosis of DR is presented. We describe all those CAD systems that have been developed by various computational intelligence and image processing techniques. The limitations and future trends of current CAD systems are also described in detail to help researchers. Moreover, potential CAD systems are also compared in terms of statistical parameters to quantitatively evaluate them. The comparison results indicate that there is still a need for accurate development of CAD systems to assist in the clinical diagnosis of diabetic retinopathy.

show abstract

Training Deep Convolutional Neural Networks with Active Learning for Exudate Classification in Eye Fundus Images

Cited by 17 publications

References 10 publications

Convolutional Neural Networks for the Automatic Identification of Plant Diseases

Convolutional Neural Networks for the Automatic Identification of Plant Diseases

Transfer learning with multiple pre-trained network for fundus classification

Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

Contact Info

Product

Resources

About