Maheen Anwar¹, Saima Farhan¹, Yasin Ul Haq², Waqar Azeem³, Muhammad Ilyas⁴, Razvan Cristian Voicu^5,*, Muhammad Hassan Tanveer⁵

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3477-3502, 2025, DOI:10.32604/cmc.2025.065141 - 03 July 2025

Abstract Glaucoma, a chronic eye disease affecting millions worldwide, poses a substantial threat to eyesight and can result in permanent vision loss if left untreated. Manual identification of glaucoma is a complicated and time-consuming practice requiring specialized expertise and results may be subjective. To address these challenges, this research proposes a computer-aided diagnosis (CAD) approach using Artificial Intelligence (AI) techniques for binary and multiclass classification of glaucoma stages. An ensemble fusion mechanism that combines the outputs of three pre-trained convolutional neural network (ConvNet) models–ResNet-50, VGG-16, and InceptionV3 is utilized in this paper. This fusion technique enhances… More >

Kachi Anvesh^1,2, Bharati M. Reshmi^2,3, Shanmugasundaram Hariharan⁴, H. Venkateshwara Reddy⁵, Murugaperumal Krishnamoorthy⁶, Vinay Kukreja⁷, Shih-Yu Chen^8,9,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1485-1517, 2025, DOI:10.32604/cmes.2025.063239 - 30 May 2025

Abstract Automated classification of retinal fundus images is essential for identifying eye diseases, though there is earlier research on applying deep learning models designed especially for detecting tessellation in retinal fundus images. This study classifies 4 classes of retinal fundus images with 3 diseased fundus images and 1 normal fundus image, by creating a refined VGG16 model to categorize fundus pictures into tessellated, normal, myopia, and choroidal neovascularization groups. The approach utilizes a VGG16 architecture that has been altered with unique fully connected layers and regularization using dropouts, along with data augmentation techniques (rotation, flip, and… More >

Jahanzaib Latif ¹, Ahsan Wajahat¹, Alishba Tahir², Anas Bilal^3,*, Mohammed Zakariah⁴, Abeer Alnuaim⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 539-567, 2025, DOI:10.32604/cmes.2025.062301 - 11 April 2025

Abstract Glaucoma, a leading cause of blindness, demands early detection for effective management. While AI-based diagnostic systems are gaining traction, their performance is often limited by challenges such as varying image backgrounds, pixel intensity inconsistencies, and object size variations. To address these limitations, we introduce an innovative, nature-inspired machine learning framework combining feature excitation-based dense segmentation networks (FEDS-Net) and an enhanced gray wolf optimization-supported support vector machine (IGWO-SVM). This dual-stage approach begins with FEDS-Net, which utilizes a fuzzy integral (FI) technique to accurately segment the optic cup (OC) and optic disk (OD) from retinal images, even More >

Anandhavalli Muniasamy^1,*, Ashwag Alasmari²

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 569-592, 2025, DOI:10.32604/cmes.2025.060484 - 11 April 2025

Abstract The effective and timely diagnosis and treatment of ocular diseases are key to the rapid recovery of patients. Today, the mass disease that needs attention in this context is cataracts. Although deep learning has significantly advanced the analysis of ocular disease images, there is a need for a probabilistic model to generate the distributions of potential outcomes and thus make decisions related to uncertainty quantification. Therefore, this study implements a Bayesian Convolutional Neural Networks (BCNN) model for predicting cataracts by assigning probability values to the predictions. It prepares convolutional neural network (CNN) and BCNN models. More > Graphic Abstract

Mustafa Youldash¹, Atta Rahman^2,*, Manar Alsayed¹, Abrar Sebiany¹, Joury Alzayat¹, Noor Aljishi¹, Ghaida Alshammari¹, Mona Alqahtani¹

Intelligent Automation & Soft Computing, Vol.40, pp. 125-143, 2025, DOI:10.32604/iasc.2025.058509 - 23 January 2025

Abstract Diabetic retinopathy (DR) is a complication of diabetes that can lead to reduced vision or even blindness if left untreated. Therefore, early and accurate detection of this disease is crucial for diabetic patients to prevent vision loss. This study aims to develop a deep-learning approach for the early and precise diagnosis of DR, as manual detection can be time-consuming, costly, and prone to human error. The classification task is divided into two groups for binary classification: patients with DR (diagnoses 1–4) and those without DR (diagnosis 0). For multi-class classification, the categories are no DR,… More >

Atta Rahman^1,*, Mustafa Youldash², Ghaida Alshammari², Abrar Sebiany², Joury Alzayat², Manar Alsayed², Mona Alqahtani², Noor Aljishi²

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4561-4576, 2024, DOI:10.32604/cmc.2024.055106 - 12 September 2024

Abstract Diabetes is a serious health condition that can cause several issues in human body organs such as the heart and kidney as well as a serious eye disease called diabetic retinopathy (DR). Early detection and treatment are crucial to prevent complete blindness or partial vision loss. Traditional detection methods, which involve ophthalmologists examining retinal fundus images, are subjective, expensive, and time-consuming. Therefore, this study employs artificial intelligence (AI) technology to perform faster and more accurate binary classifications and determine the presence of DR. In this regard, we employed three promising machine learning models namely, support… More >

Syed Ayaz Ali Shah^1,*, Aamir Shahzad^1,*, Musaed Alhussein², Chuan Meng Goh³, Khursheed Aurangzeb², Tong Boon Tang⁴, Muhammad Awais⁵

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2565-2583, 2024, DOI:10.32604/cmc.2024.047597 - 15 May 2024

Abstract Diagnosing various diseases such as glaucoma, age-related macular degeneration, cardiovascular conditions, and diabetic retinopathy involves segmenting retinal blood vessels. The task is particularly challenging when dealing with color fundus images due to issues like non-uniform illumination, low contrast, and variations in vessel appearance, especially in the presence of different pathologies. Furthermore, the speed of the retinal vessel segmentation system is of utmost importance. With the surge of now available big data, the speed of the algorithm becomes increasingly important, carrying almost equivalent weightage to the accuracy of the algorithm. To address these challenges, we present… More > Graphic Abstract

Anas Bilal¹, Azhar Imran², Talha Imtiaz Baig^3,4, Xiaowen Liu^1,*, Haixia Long¹, Abdulkareem Alzahrani⁵, Muhammad Shafiq⁶

Computer Systems Science and Engineering, Vol.48, No.2, pp. 511-528, 2024, DOI:10.32604/csse.2023.039672 - 19 March 2024

Abstract Artificial Intelligence (AI) is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy (VTDR), which is a leading cause of visual impairment and blindness worldwide. However, previous automated VTDR detection methods have mainly relied on manual feature extraction and classification, leading to errors. This paper proposes a novel VTDR detection and classification model that combines different models through majority voting. Our proposed methodology involves preprocessing, data augmentation, feature extraction, and classification stages. We use a hybrid convolutional neural network-singular value decomposition (CNN-SVD) model for feature extraction and selection and an improved SVM-RBF with a Decision Tree More >

Hengyang Liu, Chuncheng Huang^*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1745-1761, 2023, DOI:10.32604/cmc.2023.040710 - 30 August 2023

Abstract In the field of medical images, pixel-level labels are time-consuming and expensive to acquire, while image-level labels are relatively easier to obtain. Therefore, it makes sense to learn more information (knowledge) from a small number of hard-to-get pixel-level annotated images to apply to different tasks to maximize their usefulness and save time and training costs. In this paper, using Pixel-Level Labeled Images for Multi-Task Learning (PLDMLT), we focus on grading the severity of fundus images for Diabetic Retinopathy (DR). This is because, for the segmentation task, there is a finely labeled mask, while the severity… More >

Arfat Ahmad Khan¹, Shtwai Alsubai², Chitapong Wechtaisong^3,*, Ahmad Almadhor⁴, Natalia Kryvinska^5,*, Abdullah Al Hejaili⁶, Uzma Ghulam Mohammad⁷

Computer Systems Science and Engineering, Vol.47, No.2, pp. 1733-1750, 2023, DOI:10.32604/csse.2023.039296 - 28 July 2023

Abstract A cataract is one of the most significant eye problems worldwide that does not immediately impair vision and progressively worsens over time. Automatic cataract prediction based on various imaging technologies has been addressed recently, such as smartphone apps used for remote health monitoring and eye treatment. In recent years, advances in diagnosis, prediction, and clinical decision support using Artificial Intelligence (AI) in medicine and ophthalmology have been exponential. Due to privacy concerns, a lack of data makes applying artificial intelligence models in the medical field challenging. To address this issue, a federated learning framework named CD-FL… More >