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  • Open Access

    ARTICLE

    Optimizing Fully Convolutional Encoder-Decoder Network for Segmentation of Diabetic Eye Disease

    Abdul Qadir Khan1, Guangmin Sun1,*, Yu Li1, Anas Bilal2, Malik Abdul Manan1

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2481-2504, 2023, DOI:10.32604/cmc.2023.043239 - 29 November 2023

    Abstract In the emerging field of image segmentation, Fully Convolutional Networks (FCNs) have recently become prominent. However, their effectiveness is intimately linked with the correct selection and fine-tuning of hyperparameters, which can often be a cumbersome manual task. The main aim of this study is to propose a more efficient, less labour-intensive approach to hyperparameter optimization in FCNs for segmenting fundus images. To this end, our research introduces a hyperparameter-optimized Fully Convolutional Encoder-Decoder Network (FCEDN). The optimization is handled by a novel Genetic Grey Wolf Optimization (G-GWO) algorithm. This algorithm employs the Genetic Algorithm (GA) to… More >

  • Open Access

    ARTICLE

    Deep Learning with a Novel Concoction Loss Function for Identification of Ophthalmic Disease

    Sayyid Kamran Hussain1, Ali Haider Khan2,*, Malek Alrashidi3, Sajid Iqbal4, Qazi Mudassar Ilyas4, Kamran Shah5

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3763-3781, 2023, DOI:10.32604/cmc.2023.041722 - 08 October 2023

    Abstract As ocular computer-aided diagnostic (CAD) tools become more widely accessible, many researchers are developing deep learning (DL) methods to aid in ocular disease (OHD) diagnosis. Common eye diseases like cataracts (CATR), glaucoma (GLU), and age-related macular degeneration (AMD) are the focus of this study, which uses DL to examine their identification. Data imbalance and outliers are widespread in fundus images, which can make it difficult to apply many DL algorithms to accomplish this analytical assignment. The creation of effcient and reliable DL algorithms is seen to be the key to further enhancing detection performance. Using… More >

  • Open Access

    ARTICLE

    Spatial Correlation Module for Classification of Multi-Label Ocular Diseases Using Color Fundus Images

    Ali Haider Khan1,2,*, Hassaan Malik2, Wajeeha Khalil3, Sayyid Kamran Hussain4, Tayyaba Anees5, Muzammil Hussain2

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 133-150, 2023, DOI:10.32604/cmc.2023.039518 - 08 June 2023

    Abstract To prevent irreversible damage to one’s eyesight, ocular diseases (ODs) need to be recognized and treated immediately. Color fundus imaging (CFI) is a screening technology that is both effective and economical. According to CFIs, the early stages of the disease are characterized by a paucity of observable symptoms, which necessitates the prompt creation of automated and robust diagnostic algorithms. The traditional research focuses on image-level diagnostics that attend to the left and right eyes in isolation without making use of pertinent correlation data between the two sets of eyes. In addition, they usually only target… More >

  • Open Access

    ARTICLE

    Stage-Wise Categorization and Prediction of Diabetic Retinopathy Using Ensemble Learning and 2D-CNN

    N. M. Balamurugan1,*, K. Maithili2, T. K. S. Rathish Babu3, M. Adimoolam4

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 499-514, 2023, DOI:10.32604/iasc.2023.031661 - 29 September 2022

    Abstract Diabetic Eye Disease (DED) is a fundamental cause of blindness in human beings in the medical world. Different techniques are proposed to forecast and examine the stages in Prognostication of Diabetic Retinopathy (DR). The Machine Learning (ML) and the Deep Learning (DL) algorithms are the predominant techniques to project and explore the images of DR. Even though some solutions were adapted to challenge the cause of DR disease, still there should be an efficient and accurate DR prediction to be adapted to refine its performance. In this work, a hybrid technique was proposed for classification… More >

  • Open Access

    REVIEW

    Mesenchymal stem cell-derived exosomes as new remedy for the treatment of inflammatory eye diseases

    CARL RANDALL HARRELL1,#, ANA VOLAREVIC2,#,*, DRAGICA PAVLOVIC3, VALENTIN DJONOV4, VLADISLAV VOLAREVIC4,5,*

    BIOCELL, Vol.46, No.10, pp. 2195-2200, 2022, DOI:10.32604/biocell.2022.020175 - 13 June 2022

    Abstract Detrimental immune response has a crucially important role in the development and progression of inflammatory eye diseases. Inflammatory mediators and proteolytic enzymes released by activated immune cells induce serious injury of corneal epithelial cells and retinal ganglion cell which may result in the vision loss. Mesenchymal stem cells (MSCs) are regulatory cells which produce various immunosuppressive factors that modulate phenotype and function of inflammatory immune cells. However, several safety issues, including undesired differentiation and emboli formation, limit clinical use of MSCs. MSC-derived exosomes (MSC-Exos) are nano-sized extracellular vesicles which contain all MSC-derived immunoregulatory factors. Intraocular More >

  • Open Access

    ARTICLE

    Decision Support System for Diagnosis of Irregular Fovea

    Ghulam Ali Mallah1, Jamil Ahmed1, Muhammad Irshad Nazeer2,*, Mazhar Ali Dootio3, Hidayatullah Shaikh1, Aadil Jameel1

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5343-5353, 2022, DOI:10.32604/cmc.2022.023581 - 14 January 2022

    Abstract Detection of abnormalities in human eye is one of the well-established research areas of Machine Learning. Deep Learning techniques are widely used for the diagnosis of Retinal Diseases (RD). Fovea is one of the significant parts of retina which would be prevented before the involvement of Perforated Blood Vessels (PBV). Retinopathy Images (RI) contains sufficient information to classify structural changes incurred upon PBV but Macular Features (MF) and Fovea Features (FF) are very difficult to detect because features of MF and FF could be found with Similar Color Movements (SCM) with minor variations. This paper… More >

  • Open Access

    ARTICLE

    Improved Model of Eye Disease Recognition Based on VGG Model

    Ye Mu1,2,3,4, Yuheng Sun1, Tianli Hu1,2,3,4, He Gong1,2,3,4, Shijun Li1,2,3,4,*, Thobela Louis Tyasi5

    Intelligent Automation & Soft Computing, Vol.28, No.3, pp. 729-737, 2021, DOI:10.32604/iasc.2021.016569 - 20 April 2021

    Abstract The rapid development of computer vision technology and digital images has increased the potential for using image recognition for eye disease diagnosis. Many early screening and diagnosis methods for ocular diseases based on retinal images of the fundus have been proposed recently, but their accuracy is low. Therefore, it is important to develop and evaluate an improved VGG model for the recognition and classification of retinal fundus images. In response to these challenges, to solve the problem of accuracy and reliability of clinical algorithms in medical imaging this paper proposes an improved model for early 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 - 12 January 2021

    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… More >

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