Diabetic Retinopathy Detection and Classification Using Mixed Models for a Disease Grading Database

Alsallakh, Bilal; Sun, Guangmin; Liu, Yu; Mazhar, Sarah; Khan, Abdul Qadir

doi:10.1109/access.2021.3056186

Cited by 85 publications

(33 citation statements)

References 36 publications

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“…In general, six major distinct ML algorithms were used in these studies. These are: principal component analysis (PCA) [ 70 , 71 ], linear discriminant analysis (LDA)-based feature selection [ 71 ], spatial invariant feature transform (SIFT) [ 71 ], support vector machine (SVM) [ 16 , 71 , 72 , 73 ], k nearest neighbor (KNN) [ 72 ] and random forest (RF) [ 74 ].…”

Section: Resultsmentioning

confidence: 99%

“…principal component analysis (PCA) [70,71], linear discriminant analysis (LDA)-based feature selection [71], spatial invariant feature transform (SIFT) [71], support-vector-machine (SVM) [16,71,72,73], KNN [72] and Random Forest (RF) [74]. The methods in combination with DL networks improved the performance and training process.…”

Section: Machine Learning Approachesmentioning

confidence: 99%

“…Recent developments in CAD techniques, which are defined in the subscope of artificial intelligence (AI), are becoming more prominent in modern ophthalmology [ 15 ] as they can save time, cost and human resources for routine DR screening and involve lower diagnostic error factors [ 15 ]. CAD can also efficiently manage the increasing number of afflicted DR patients [ 16 ] and diagnose DR in early stages when fewer sight threatening effects are present. The scope of AI based approaches are divided between Machine Learning-based (ML) and Deep Learning-based (DL) solutions.…”

Section: Introductionmentioning

confidence: 99%

“…Recent developments in Computer-Aided Diagnosis (CAD) techniques are becoming more prominent in modern ophthalmology [15] as they can save time, cost and human resource for routine DR screening and involve lower diagnostic error factors [15]. CAD can also efficiently manage the increasing number of afflicted DR patients [16] and diagnose DR in early stages with fewer sight threatening effects. These techniques vary depending on the imaging system.…”

Section: Introductionmentioning

confidence: 99%

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Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey

et al. 2021

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Diabetic Retinopathy (DR) is a leading cause of vision loss in the world. In the past few years, artificial intelligence (AI) based approaches have been used to detect and grade DR. Early detection enables appropriate treatment and thus prevents vision loss. For this purpose, both fundus and optical coherence tomography (OCT) images are used to image the retina. Next, Deep-learning (DL)-/machine-learning (ML)-based approaches make it possible to extract features from the images and to detect the presence of DR, grade its severity and segment associated lesions. This review covers the literature dealing with AI approaches to DR such as ML and DL in classification and segmentation that have been published in the open literature within six years (2016–2021). In addition, a comprehensive list of available DR datasets is reported. This list was constructed using both the PICO (P-Patient, I-Intervention, C-Control, O-Outcome) and Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) 2009 search strategies. We summarize a total of 114 published articles which conformed to the scope of the review. In addition, a list of 43 major datasets is presented.

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Section: Resultsmentioning

confidence: 99%

Section: Machine Learning Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey

et al. 2021

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“…The recent work carried out by Zhou et al [27] has emphasized improving transfer learning function to improve outcomes of classifying segmented lesions. Bilal et al [28] emphasize detection techniques for classification. This model has presented the extraction of features as preprocessing to address the presence of abnormalities and support an effective segmentation technique.…”

Section: Related Workmentioning

confidence: 99%

Novel Framework for Enhanced Learning-based Classification of Lesion in Diabetic Retinopathy

Prakruthi¹,

Komarasamy²

2022

IJACSA

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Diabetic retinopathy is an adverse medical condition resulting from a high level of blood sugar potentially affecting the retina and leading to permanent vision loss in its advanced stage of progression. A literature review is conducted to assess the effectiveness of existing approaches to find that Convolution Neural Network (CNN) has been frequently adopted for analyzing the fundus retinal image for detection and classification. However, existing scientific methods are mainly inclined towards achieving accuracy in their learning techniques without much deeper investigation of possibilities to improve the methodology of type using CNN. Therefore, the proposed scheme introduces a computational framework where a simplified feature enhancement operation is carried out, resulting in artifact-free images with better features. The enhanced image is then subjected to CNN to perform multiclass categorization of potential stages of diabetic retinopathy to see if it outperforms existing schemes.

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An efficient early detection of diabetic retinopathy using dwarf mongoose optimization based deep belief network

Abirami

Kavitha

2022

Concurrency and Computation

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In general, diabetic retinopathy (DR) is a common ocular disease that causes damage to the retina due to blood leakage from the vessels. Earlier detection of DR becomes a complicated task and it is necessary to prevent complete blindness. Various physical examinations are employed in DR detection but manual diagnosis results in misclassification results. Therefore, this article proposes a novel technique to predict and classify the DR disease effectively. The significant objective of the proposed approach involves the effective classification of fundus retinal images into two namely, normal (absence of DR) and abnormal (presence of DR). The proposed DR detection utilizes three vital phases namely, the data preprocessing, image augmentation, feature extraction, and classification. Initially, the image preprocessing is done to remove unwanted noises and to enhance images. Then, the preprocessed image is augmented to enhance the size and quality of the training images. This article proposes a novel modified Gaussian convolutional deep belief network based dwarf mongoose optimization algorithm for effective extraction and classification of retinal images. In this article, an ODIR-2019 dataset is employed in detecting and classifying DR disease.Finally, the experimentation is carried out and the proposed approach achieved 97% of accuracy. This implies that our proposed approach effectively classifies the fundus retinal images.

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Diabetic Retinopathy Detection and Classification Using Mixed Models for a Disease Grading Database

Cited by 85 publications

References 36 publications

Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey

Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey

Novel Framework for Enhanced Learning-based Classification of Lesion in Diabetic Retinopathy

An efficient early detection of diabetic retinopathy using dwarf mongoose optimization based deep belief network

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