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  • Open Access


    Detection and Classification of Diabetic Retinopathy Using DCNN and BSN Models

    S. Sudha*, A. Srinivasan, T. Gayathri Devi

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 597-609, 2022, DOI:10.32604/cmc.2022.024065

    Abstract Diabetes is associated with many complications that could lead to death. Diabetic retinopathy, a complication of diabetes, is difficult to diagnose and may lead to vision loss. Visual identification of micro features in fundus images for the diagnosis of DR is a complex and challenging task for clinicians. Because clinical testing involves complex procedures and is time-consuming, an automated system would help ophthalmologists to detect DR and administer treatment in a timely manner so that blindness can be avoided. Previous research works have focused on image processing algorithms, or neural networks, or signal processing techniques alone to detect diabetic retinopathy.… More >

  • Open Access


    Detection of Diabetic Retinopathy Using Custom CNN to Segment the Lesions

    Saleh Albahli1,2,*, Ghulam Nabi Ahmad Hassan Yar3

    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 837-853, 2022, DOI:10.32604/iasc.2022.024427

    Abstract Diabetic retinopathy is an eye deficiency that affects the retina as a result of the patient having Diabetes Mellitus caused by high sugar levels. This condition causes the blood vessels that nourish the retina to swell and become distorted and eventually become blocked. In recent times, images have played a vital role in using convolutional neural networks to automatically detect medical conditions, retinopathy takes this to another level because there is need not for just a system that could determine is a patient has retinopathy, but also a system that could tell the severity of the procession and if it… More >

  • Open Access


    Transfer Learning-based Computer-aided Diagnosis System for Predicting Grades of Diabetic Retinopathy

    Qaisar Abbas1,*, Mostafa E. A. Ibrahim1,2, Abdul Rauf Baig1

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4573-4590, 2022, DOI:10.32604/cmc.2022.023670

    Abstract Diabetic retinopathy (DR) diagnosis through digital fundus images requires clinical experts to recognize the presence and importance of many intricate features. This task is very difficult for ophthalmologists and time-consuming. Therefore, many computer-aided diagnosis (CAD) systems were developed to automate this screening process of DR. In this paper, a CAD-DR system is proposed based on preprocessing and a pre-train transfer learning-based convolutional neural network (PCNN) to recognize the five stages of DR through retinal fundus images. To develop this CAD-DR system, a preprocessing step is performed in a perceptual-oriented color space to enhance the DR-related lesions and then a standard… More >

  • Open Access


    Diabetic Retinopathy Diagnosis Using ResNet with Fuzzy Rough C-Means Clustering

    R. S. Rajkumar*, A. Grace Selvarani

    Computer Systems Science and Engineering, Vol.42, No.2, pp. 509-521, 2022, DOI:10.32604/csse.2022.021909


    Diabetic Retinopathy (DR) is a vision disease due to the long-term prevalence of Diabetes Mellitus. It affects the retina of the eye and causes severe damage to the vision. If not treated on time it may lead to permanent vision loss in diabetic patients. Today’s development in science has no medication to cure Diabetic Retinopathy. However, if diagnosed at an early stage it can be controlled and permanent vision loss can be avoided. Compared to the diabetic population, experts to diagnose Diabetic Retinopathy are very less in particular to local areas. Hence an automatic computer-aided diagnosis for DR detection is… More >

  • Open Access


    Diabetic Retinopathy Detection Using Classical-Quantum Transfer Learning Approach and Probability Model

    Amna Mir1, Umer Yasin1, Salman Naeem Khan1, Atifa Athar3,*, Riffat Jabeen2, Sehrish Aslam1

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3733-3746, 2022, DOI:10.32604/cmc.2022.022524

    Abstract Diabetic Retinopathy (DR) is a common complication of diabetes mellitus that causes lesions on the retina that affect vision. Late detection of DR can lead to irreversible blindness. The manual diagnosis process of DR retina fundus images by ophthalmologists is time consuming and costly. While, Classical Transfer learning models are extensively used for computer aided detection of DR; however, their maintenance costs limits detection performance rate. Therefore, Quantum Transfer learning is a better option to address this problem in an optimized manner. The significance of Hybrid quantum transfer learning approach includes that it performs heuristically. Thus, our proposed methodology aims… More >

  • Open Access


    Comparative Study of Transfer Learning Models for Retinal Disease Diagnosis from Fundus Images

    Kuntha Pin1, Jee Ho Chang2, Yunyoung Nam3,*

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5821-5834, 2022, DOI:10.32604/cmc.2022.021943

    Abstract While the usage of digital ocular fundus image has been widespread in ophthalmology practice, the interpretation of the image has been still on the hands of the ophthalmologists which are quite costly. We explored a robust deep learning system that detects three major ocular diseases: diabetic retinopathy (DR), glaucoma (GLC), and age-related macular degeneration (AMD). The proposed method is composed of two steps. First, an initial quality evaluation in the classification system is proposed to filter out poor-quality images to enhance its performance, a technique that has not been explored previously. Second, the transfer learning technique is used with various… More >

  • Open Access


    A Feature Selection Strategy to Optimize Retinal Vasculature Segmentation

    José Escorcia-Gutierrez1,4,*, Jordina Torrents-Barrena4, Margarita Gamarra2, Natasha Madera1, Pedro Romero-Aroca3, Aida Valls4, Domenec Puig4

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2971-2989, 2022, DOI:10.32604/cmc.2022.020074

    Abstract Diabetic retinopathy (DR) is a complication of diabetes mellitus that appears in the retina. Clinitians use retina images to detect DR pathological signs related to the occlusion of tiny blood vessels. Such occlusion brings a degenerative cycle between the breaking off and the new generation of thinner and weaker blood vessels. This research aims to develop a suitable retinal vasculature segmentation method for improving retinal screening procedures by means of computer-aided diagnosis systems. The blood vessel segmentation methodology relies on an effective feature selection based on Sequential Forward Selection, using the error rate of a decision tree classifier in the… More >

  • Open Access


    Enhanced Detection of Glaucoma on Ensemble Convolutional Neural Network for Clinical Informatics

    D. Stalin David1,*, S. Arun Mozhi Selvi2, S. Sivaprakash3, P. Vishnu Raja4, Dilip Kumar Sharma5, Pankaj Dadheech6, Sudhakar Sengan7

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2563-2579, 2022, DOI:10.32604/cmc.2022.020059

    Abstract Irretrievable loss of vision is the predominant result of Glaucoma in the retina. Recently, multiple approaches have paid attention to the automatic detection of glaucoma on fundus images. Due to the interlace of blood vessels and the herculean task involved in glaucoma detection, the exactly affected site of the optic disc of whether small or big size cup, is deemed challenging. Spatially Based Ellipse Fitting Curve Model (SBEFCM) classification is suggested based on the Ensemble for a reliable diagnosis of Glaucoma in the Optic Cup (OC) and Optic Disc (OD) boundary correspondingly. This research deploys the Ensemble Convolutional Neural Network… More >

  • Open Access


    A Multilevel Deep Feature Selection Framework for Diabetic Retinopathy Image Classification

    Farrukh Zia1, Isma Irum1, Nadia Nawaz Qadri1, Yunyoung Nam2,*, Kiran Khurshid3, Muhammad Ali1, Imran Ashraf4, Muhammad Attique Khan4

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2261-2276, 2022, DOI:10.32604/cmc.2022.017820

    Abstract Diabetes or Diabetes Mellitus (DM) is the upset that happens due to high glucose level within the body. With the passage of time, this polygenic disease creates eye deficiency referred to as Diabetic Retinopathy (DR) which can cause a major loss of vision. The symptoms typically originate within the retinal space square in the form of enlarged veins, liquid dribble, exudates, haemorrhages and small scale aneurysms. In current therapeutic science, pictures are the key device for an exact finding of patients’ illness. Meanwhile, an assessment of new medicinal symbolisms stays complex. Recently, Computer Vision (CV) with deep neural networks can… More >

  • Open Access


    Intelligent and Integrated Framework for Exudate Detection in Retinal Fundus Images

    Muhammad Shujaat1, Numan Aslam1, Iram Noreen1, Muhammad Khurram Ehsan1,*, Muhammad Aasim Qureshi1, Aasim Ali1, Neelma Naz2, Imtisal Qadeer3

    Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 663-672, 2021, DOI:10.32604/iasc.2021.019194

    Abstract Diabetic Retinopathy (DR) is a disease of the retina caused by diabetes. The existence of exudates in the retina is the primary visible sign of DR. Early exudate detection can prevent patients from the severe conditions of DR An intelligent framework is proposed that serves two purposes. First, it highlights the features of exudate from fundus images using an image processing approach. Afterwards, the enhanced features are used as input to train Alexnet for the detection of exudates. The proposed framework is comprised on three stages that include pre-processing, image segmentation, and classification. During the pre-processing stage, image quality is… More >

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