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  • Open Access

    ARTICLE

    HCSP-Net: A Novel Model of Age-Related Macular Degeneration Classification Based on Color Fundus Photography

    Cheng Wan1, Jiani Zhao1, Xiangqian Hong2, Weihua Yang2,*, Shaochong Zhang2,*

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 391-407, 2024, DOI:10.32604/cmc.2024.048307 - 25 April 2024

    Abstract Age-related macular degeneration (AMD) ranks third among the most common causes of blindness. As the most conventional and direct method for identifying AMD, color fundus photography has become prominent owing to its consistency, ease of use, and good quality in extensive clinical practice. In this study, a convolutional neural network (CSPDarknet53) was combined with a transformer to construct a new hybrid model, HCSP-Net. This hybrid model was employed to tri-classify color fundus photography into the normal macula (NM), dry macular degeneration (DMD), and wet macular degeneration (WMD) based on clinical classification manifestations, thus identifying and… More >

  • Open Access

    ARTICLE

    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 - 11 October 2021

    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… 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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