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  • Open Access

    ARTICLE

    Comparison of Detection and Classification of Hard Exudates Using Artificial Neural System vs. SVM Radial Basis Function in Diabetic Retinopathy

    V. Sudha1,*, T. R. Ganesh Babu2, N. Vikram1, R. Raja2

    Molecular & Cellular Biomechanics, Vol.18, No.3, pp. 139-145, 2021, DOI:10.32604/mcb.2021.016056

    Abstract Diabetic Retinopathy (DR) is a disease that occurs in the eye which results in blindness as it passes to proliferative stage. Diabetes can significantly result in symptoms like blurring of vision, kidney failure, nervous damage. Hence it has become necessary to identify retinal damage that occurs in diabetic eye due to raised glucose level in its initial stage itself. Hence automated detection of anamoly has become very essential. The appearance of crimson and yellow lesions is considered as the earliest symptoms of DR which are called as hemorrhages and exudates. If DR is analysed at initial stage, blindness does not… More >

  • Open Access

    ARTICLE

    Recognition and Detection of Diabetic Retinopathy Using Densenet-65 Based Faster-RCNN

    Saleh Albahli1, Tahira Nazir2,*, Aun Irtaza2, Ali Javed3

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1333-1351, 2021, DOI:10.32604/cmc.2021.014691

    Abstract Diabetes is a metabolic disorder that results in a retinal complication called diabetic retinopathy (DR) which is one of the four main reasons for sightlessness all over the globe. DR usually has no clear symptoms before the onset, thus making disease identification a challenging task. The healthcare industry may face unfavorable consequences if the gap in identifying DR is not filled with effective automation. Thus, our objective is to develop an automatic and cost-effective method for classifying DR samples. In this work, we present a custom Faster-RCNN technique for the recognition and classification of DR lesions from retinal images. After… More >

  • Open Access

    ARTICLE

    Classification of Fundus Images Based on Deep Learning for Detecting Eye Diseases

    Nakhim Chea1, Yunyoung Nam2,*

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 411-426, 2021, DOI:10.32604/cmc.2021.013390

    Abstract Various techniques to diagnose eye diseases such as diabetic retinopathy (DR), glaucoma (GLC), and age-related macular degeneration (AMD), are possible through deep learning algorithms. A few recent studies have examined a couple of major diseases and compared them with data from healthy subjects. However, multiple major eye diseases, such as DR, GLC, and AMD, could not be detected simultaneously by computer-aided systems to date. There were just high-performance-outcome researches on a pair of healthy and eye-diseased group, besides of four categories of fundus image classification. To have a better knowledge of multi-categorical classification of fundus photographs, we used optimal residual… More >

  • Open Access

    REVIEW

    Detection and Grading of Diabetic Retinopathy in Retinal Images Using Deep Intelligent Systems: A Comprehensive Review

    H. Asha Gnana Priya1, J. Anitha1, Daniela Elena Popescu2, Anju Asokan1, D. Jude Hemanth1, Le Hoang Son3,4,*

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2771-2786, 2021, DOI:10.32604/cmc.2021.012907

    Abstract Diabetic Retinopathy (DR) is an eye disease that mainly affects people with diabetes. People affected by DR start losing their vision from an early stage even though the symptoms are identified only at the later stage. Once the vision is lost, it cannot be regained but can be prevented from causing any further damage. Early diagnosis of DR is required for preventing vision loss, for which a trained ophthalmologist is required. The clinical practice is time-consuming and is not much successful in identifying DR at early stages. Hence, Computer-Aided Diagnosis (CAD) system is a suitable alternative for screening and grading… More >

  • Open Access

    ARTICLE

    An Optimal Deep Learning Based Computer-Aided Diagnosis System for Diabetic Retinopathy

    Phong Thanh Nguyen1, Vy Dang Bich Huynh2, Khoa Dang Vo1, Phuong Thanh Phan1, Eunmok Yang3,*, Gyanendra Prasad Joshi4

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2815-2830, 2021, DOI:10.32604/cmc.2021.012315

    Abstract Diabetic Retinopathy (DR) is a significant blinding disease that poses serious threat to human vision rapidly. Classification and severity grading of DR are difficult processes to accomplish. Traditionally, it depends on ophthalmoscopically-visible symptoms of growing severity, which is then ranked in a stepwise scale from no retinopathy to various levels of DR severity. This paper presents an ensemble of Orthogonal Learning Particle Swarm Optimization (OPSO) algorithm-based Convolutional Neural Network (CNN) Model EOPSO-CNN in order to perform DR detection and grading. The proposed EOPSO-CNN model involves three main processes such as preprocessing, feature extraction, and classification. The proposed model initially involves… More >

  • Open Access

    ARTICLE

    Intelligent Prediction Approach for Diabetic Retinopathy Using Deep Learning Based Convolutional Neural Networks Algorithm by Means of Retina Photographs

    G. Arun Sampaul Thomas1, Y. Harold Robinson2, E. Golden Julie3, Vimal Shanmuganathan4, Seungmin Rho5, Yunyoung Nam6,*

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1613-1629, 2021, DOI:10.32604/cmc.2020.013443

    Abstract Retinopathy is a human eye disease that causes changes in retinal blood vessels that leads to bleed, leak fluid and vision impairment. Symptoms of retinopathy are blurred vision, changes in color perception, red spots, and eye pain and it cannot be detected with a naked eye. In this paper, a new methodology based on Convolutional Neural Networks (CNN) is developed and proposed to intelligent retinopathy prediction and give a decision about the presence of retinopathy with automatic diabetic retinopathy screening with accurate diagnoses. The CNN model is trained by different images of eyes that have retinopathy and those which do… More >

  • Open Access

    ARTICLE

    An IoT-Cloud Based Intelligent Computer-Aided Diagnosis of Diabetic Retinopathy Stage Classification Using Deep Learning Approach

    K. Shankar1,*, Eswaran Perumal1, Mohamed Elhoseny2, Phong Thanh Nguyen3

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1665-1680, 2021, DOI:10.32604/cmc.2020.013251

    Abstract Diabetic retinopathy (DR) is a disease with an increasing prevalence and the major reason for blindness among working-age population. The possibility of severe vision loss can be extensively reduced by timely diagnosis and treatment. An automated screening for DR has been identified as an effective method for early DR detection, which can decrease the workload associated to manual grading as well as save diagnosis costs and time. Several studies have been carried out to develop automated detection and classification models for DR. This paper presents a new IoT and cloud-based deep learning for healthcare diagnosis of Diabetic Retinopathy (DR). The… More >

  • Open Access

    ARTICLE

    Early Detection of Diabetic Retinopathy Using Machine Intelligence through Deep Transfer and Representational Learning

    Fouzia Nawaz1, Muhammad Ramzan1, Khalid Mehmood1, Hikmat Ullah Khan2, Saleem Hayat Khan3,4, Muhammad Raheel Bhutta5,*

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1631-1645, 2021, DOI:10.32604/cmc.2020.012887

    Abstract Diabetic retinopathy (DR) is a retinal disease that causes irreversible blindness. DR occurs due to the high blood sugar level of the patient, and it is clumsy to be detected at an early stage as no early symptoms appear at the initial level. To prevent blindness, early detection and regular treatment are needed. Automated detection based on machine intelligence may assist the ophthalmologist in examining the patients’ condition more accurately and efficiently. The purpose of this study is to produce an automated screening system for recognition and grading of diabetic retinopathy using machine learning through deep transfer and representational learning.… More >

  • Open Access

    ARTICLE

    A Convolutional Neural Network Classifier VGG-19 Architecture for Lesion Detection and Grading in Diabetic Retinopathy Based on Deep Learning

    V. Sudha1,*, T. R. Ganeshbabu2

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 827-842, 2021, DOI:10.32604/cmc.2020.012008

    Abstract Diabetic Retinopathy (DR) is a type of disease in eyes as a result of a diabetic condition that ends up damaging the retina, leading to blindness or loss of vision. Morphological and physiological retinal variations involving slowdown of blood flow in the retina, elevation of leukocyte cohesion, basement membrane dystrophy, and decline of pericyte cells, develop. As DR in its initial stage has no symptoms, early detection and automated diagnosis can prevent further visual damage. In this research, using a Deep Neural Network (DNN), segmentation methods are proposed to detect the retinal defects such as exudates, hemorrhages, microaneurysms from digital… More >

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