LBP and Machine Learning for Diabetic Retinopathy Detection

Calleja, Jorge de la; Tecuapetla, Lourdes; Medina, Ma. Auxilio; Bárcenas, Everardo; Nájera, Argelia Berenice Urbina

doi:10.1007/978-3-319-10840-7_14

Cited by 31 publications

(9 citation statements)

References 7 publications

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“…Adarsh et al [20] recognized the retinal blood vessels and pathologies (exudates and MAs) from fundus images as DR features and classified the DR severity grades by the SVMs. De la et al [13] employed the local binary patterns (LBP) to extract local features and trained the random forest classifier for DR detection, which achieves the excellent performance using 71 fundus images. All of these show great potential with the development of methods for DR grading, however, they excessively depend on prior knowledge.…”

Section: Related Workmentioning

confidence: 99%

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Coarse-to-fine classification for diabetic retinopathy grading using convolutional neural network

Shi

Chen

et al. 2020

Artificial Intelligence in Medicine

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Diabetic retinopathy (DR) is the most common eye complication of diabetes and one of the leading causes of blindness and vision impairment. Automated and accurate DR grading is of great significance for the timely and effective treatment of fundus diseases. Current clinical methods remain subject to potential time-consumption and high-risk. In this paper, a hierarchically Coarse-to-fine network (CF-DRNet) is proposed as an automatic clinical tool to classify five stages of DR severity grades using convolutional neural networks (CNNs). The CF-DRNet conforms to the hierarchical characteristic of DR grading and effectively improves the classification performance of five-class DR grading, which consists of the following: (1) The Coarse Network performs two-class classification including No DR and DR, where the attention gate module highlights the salient lesion features and suppresses irrelevant background information. (2) The Fine Network is proposed to classify four stages of DR severity grades of the grade DR from the Coarse Network including mild, moderate, severe non-proliferative DR (NPDR) and proliferative DR (PDR). Experimental results show that proposed CF-DRNet outperforms some stateof-art methods in the publicly available IDRiD and Kaggle fundus image datasets. These results indicate our method enables an efficient and reliable DR grading diagnosis in clinic.

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Section: Related Workmentioning

confidence: 99%

“…The current methods for DR grading have achieved significantly improved performance [2], [13]. However, accurate classification for DR grading remains challenges because: 1) the insufficiency of training samples limits the classification performance of automatic DR grading [14].…”

Section: Introductionmentioning

confidence: 99%

Coarse-to-fine classification for diabetic retinopathy grading using convolutional neural network

Shi

Chen

et al. 2020

Artificial Intelligence in Medicine

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“…eir experiments showed 99.07% accuracy, 89% sensitivity, and 99% specificity. In 2014, de la Calleja et al [38] used local binary patterns (LBP) to extract local features and artificial neural networks, random forest (RF), and support vector machines (SVM) for detection. In using a dataset containing 71 images, their best result achieved 97.46% accuracy with RF.…”

Section: Literature Review Of Diabetic Retinopathy Detectionmentioning

confidence: 99%

Computer-Assisted Diagnosis for Diabetic Retinopathy Based on Fundus Images Using Deep Convolutional Neural Network

Yeh

Chen

et al. 2019

Mobile Information Systems

108

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Diabetic retinopathy (DR) is a complication of long-standing diabetes, which is hard to detect in its early stage because it only shows a few symptoms. Nowadays, the diagnosis of DR usually requires taking digital fundus images, as well as images using optical coherence tomography (OCT). Since OCT equipment is very expensive, it will benefit both the patients and the ophthalmologists if an accurate diagnosis can be made, based solely on reading digital fundus images. In the paper, we present a novel algorithm based on deep convolutional neural network (DCNN). Unlike the traditional DCNN approach, we replace the commonly used max-pooling layers with fractional max-pooling. Two of these DCNNs with a different number of layers are trained to derive more discriminative features for classification. After combining features from metadata of the image and DCNNs, we train a support vector machine (SVM) classifier to learn the underlying boundary of distributions of each class. For the experiments, we used the publicly available DR detection database provided by Kaggle. We used 34,124 training images and 1,000 validation images to build our model and tested with 53,572 testing images. The proposed DR classifier classifies the stages of DR into five categories, labeled with an integer ranging between zero and four. The experimental results show that the proposed method can achieve a recognition rate up to 86.17%, which is higher than previously reported in the literature. In addition to designing a machine learning algorithm, we also develop an app called “Deep Retina.” Equipped with a handheld ophthalmoscope, the average person can take fundus images by themselves and obtain an immediate result, calculated by our algorithm. It is beneficial for home care, remote medical care, and self-examination.

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“…For the computation of LBP, the uniform pattern is used so that the separate labels are used for each even pattern and all non-uniform patterns are labeled under a single label. All the uneven patterns are accumulated in a single bin yield an LBP operator [14], [15]. Each bin of LBP can be regarded as micro-texton.…”

Section: B Local Binary Patternmentioning

confidence: 99%

ANN and SVM to recognize Texture features for spontaneous Detection and Rating of Diabetic Retinopathy

Jadhav*,

Shaikh

2019

IJEAT

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The higher levels of blood glucose most often causes a metabolic disorder commonly called as Diabetes, scientifically as Diabetes Mellitus. A consequence of this is a major loss of vision and in long terms may eventually cause complete blindness. It initiates with swelling on blood vessels, formation of microaneurysms at the end of narrow capillaries. Haemorrhages due to rupture of small vessels and fluid leak causes exudates. The specialist examines it to diagnose and gives proper treatment. Fundus images are the fundamental tool for proper diagnosis of patients by medical experts. In this research work the fundus images are taken for processing, the neural network and support vector machine are trained for the proposed model. The features are extracted from the diabetic retinopathy image by using texture based algorithms such as Gabor, Local binary pattern and Gray level co-occurrence matrix for rating the level of diabetic retinopathy. The performance of all methods is calculated based on accuracy, precision, Recall and f-measure.

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LBP and Machine Learning for Diabetic Retinopathy Detection

Cited by 31 publications

References 7 publications

Coarse-to-fine classification for diabetic retinopathy grading using convolutional neural network

Coarse-to-fine classification for diabetic retinopathy grading using convolutional neural network

Computer-Assisted Diagnosis for Diabetic Retinopathy Based on Fundus Images Using Deep Convolutional Neural Network

ANN and SVM to recognize Texture features for spontaneous Detection and Rating of Diabetic Retinopathy

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