Automated Diabetic Retinopathy Detection Using Bag of Words Approach

Islam, Monzurul; Dinh, Anh; Wahid, Khan A.

doi:10.4236/jbise.2017.105b010

Cited by 31 publications

(11 citation statements)

References 13 publications

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“…For a classification purpose, the data split into %65 and %35 training-testing sets. The performance achieved by SVM classifier (Islam et al, 2017). Table 6, represents a list contains a performance measurement comparison between some recently related works and our approached method for automatically diagnosis DR.…”

Section: Discussionmentioning

confidence: 99%

Intelligent System for Screening Diabetic Retinopathy by Using Neutrosophic and Statistical Fundus Image Features.

2019

ZJPAS

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Diabetic retinopathy (DR) is considered as one of the global diseases of blindness, especially for aged people. The main reason behind this disease is the complication of diabetes in retinal blood vessels. Usually, the early warning signs are not observed. Screening is an important key for the diagnosis of early stages of diabetic retinopathy. In this work, a new technique for automatically screening three categories; Normal, Non-Proliferative Diabetic Retinopathy (Non-PDR), and Proliferative Diabetic Retinopathy (PDR) disease is presented that is may help doctors and physicians to make a preliminary decision. Neutrosophic set (NS) domain based on statistical features, Gray Level Cooccurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), and difference statistics were used for features extraction. More than thirty statistical textural features derived from the NS set domain and spatial domain have been tested using a features selection scheme named one-way analysis of variables (ANOVA1) with significance value (p<0.001). After feature selection, about sixteen features were passed the test and introduced to the classification stage which is made up of three techniques Multi-class support vector machine (MUSVM), Naïve Bayes (NB), and Decision Forest (DF) classifiers. Over 50 images from each category were downloaded from Digital Retinal Images for Vessel Extraction (DRIVE) database. The performance resulted for this proposed method shows the system robustness in identifying each stage of diabetic retinopathy within the accuracy, sensitivity, and specificity about 95.5%, 100%, and 93.3% respectively. The results of this method were compared with other considered systems. The fair comparison of results shows system superiority and can be used in clinical observation.

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Section: Discussionmentioning

confidence: 99%

Intelligent System for Screening Diabetic Retinopathy by Using Neutrosophic and Statistical Fundus Image Features.

2019

ZJPAS

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“…These component vectors are used by maximum margin SVM classifier. On a similar structure M. Islam et.al in [26] included additional progression at the outset as image preprocessing. On the equivalent lines, DR recognition and severity ranking by using the blend of BOVW model with multiclass classifiers proposed by Sagar Honnungar et.al [27].…”

Section: G Multiple Instance Learning (Mil) Framework -Bag Of Visualmentioning

confidence: 99%

“…Multi class classification is performed by using multinomial logistic regression, SVM and random forest. The previously mentioned [25] [26] lesion based referable strategy is having the three phases, which incorporates acknowledgment of individual lesions, blend of individual lesions responses and final classifier. So it requires two decision makers one for the individual lesions and other for conclusive order.…”

Section: G Multiple Instance Learning (Mil) Framework -Bag Of Visualmentioning

confidence: 99%

Detection of Lesions for Diabetic Retinopathy By using Machine Learning Algorithms

Jebran P*,

Gupta

2020

IJRTE

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In this paper existing writing for computer added diagnosis (CAD) based identification of lesions that might be connected in the early finding of Diabetic Retinopathy (DR) is talked about. The recognition of sores, for example, Microaneurysms (MA), Hemorrhages (HEM) and Exudates (EX) are incorporated in this paper. A range of methodologies starting from conventional morphology to deep learning techniques have been discussed. The different strategies like hand crafted feature extraction to automated CNN based component extraction, single lesion identification to multi sore recognition have been explored. The different stages in each methods beginning from the image preprocessing to classification stage are investigated. The exhibition of the proposed strategies are outlined by various performance measurement parameters and their used data sets are tabulated. Toward the end we examined the future headings.

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“…The identification by repeated examination of patients affected of these initial lesions (mainly Microaneurysms and small blood cells) is expected as a new possibility of improving retinopathy treatment. Floating and flashes, blurred vision, and loss of sudden vision can be common symptoms of diabetic retinopathy [4].…”

Section: Introductionmentioning

confidence: 99%

Diabetic Retinopathy Detection Using Prognosis of Microaneurysm and Early Diagnosis System for Non-Proliferative Diabetic Retinopathy Based on Deep Learning Algorithms

2020

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Predicting the presence of Microaneurysms in the fundus images and the identification of diabetic retinopathy in early-stage has always been a major challenge for decades. Diabetic Retinopathy (DR) is affected by prolonged high blood glucose level which leads to microvascular complications and irreversible vision loss. Microaneurysms formation and macular edema in the retinal is the initial sign of DR and diagnosis at the right time can reduce the risk of non proliferated diabetic retinopathy. The rapid improvement of deep learning makes it gradually become an efficient technique to provide an interesting solution for medical image analysis problems. The proposed system analysis the presence of microaneurysm in fundus image using convolutional neural network algorithms that embeds deep learning as a core component accelerated with GPU(Graphics Processing Unit) which will perform medical image detection and segmentation with high-performance and low-latency inference. The semantic segmentation algorithm is utilized to classify the fundus picture as normal or infected. Semantic segmentation divides the image pixels based on their common semantic to identify the feature of microaneurysm. This provides an automated system that will assist ophthalmologists to grade the fundus images as early NPDR, moderate NPDR, and severe NPDR. The Prognosis of Microaneurysm and early diagnosis system for non-proliferative diabetic retinopathy system has been proposed that is capable to train effectively a deep convolution neural network for semantic segmentation of fundus images which can increase the efficiency and accuracy of NPDR (non proliferated diabetic retinopathy) prediction.

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Automated Diabetic Retinopathy Detection Using Bag of Words Approach

Cited by 31 publications

References 13 publications

Intelligent System for Screening Diabetic Retinopathy by Using Neutrosophic and Statistical Fundus Image Features.

Intelligent System for Screening Diabetic Retinopathy by Using Neutrosophic and Statistical Fundus Image Features.

Detection of Lesions for Diabetic Retinopathy By using Machine Learning Algorithms

Diabetic Retinopathy Detection Using Prognosis of Microaneurysm and Early Diagnosis System for Non-Proliferative Diabetic Retinopathy Based on Deep Learning Algorithms

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