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

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 (DT) and K-Nearest Neighbor (KNN)… More >

V. Banupriya^1,*, S. Anusuya²

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5597-5613, 2023, DOI:10.32604/cmc.2023.038696

Abstract In the modern world, one of the most severe eye infections brought on by diabetes is known as diabetic retinopathy (DR), which will result in retinal damage, and, thus, lead to blindness. Diabetic retinopathy (DR) can be well treated with early diagnosis. Retinal fundus images of humans are used to screen for lesions in the retina. However, detecting DR in the early stages is challenging due to the minimal symptoms. Furthermore, the occurrence of diseases linked to vascular anomalies brought on by DR aids in diagnosing the condition. Nevertheless, the resources required for manually identifying the lesions are high. Similarly,… More >

Mohammad Yamin^1,*, Sarah Basahel¹, Saleh Bajaba², Mona Abusurrah³, E. Laxmi Lydia⁴

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1901-1916, 2023, DOI:10.32604/csse.2023.036455

Abstract Recently, there has been a considerable rise in the number of diabetic patients suffering from diabetic retinopathy (DR). DR is one of the most chronic diseases and makes the key cause of vision loss in middle-aged people in the developed world. Initial detection of DR becomes necessary for decreasing the disease severity by making use of retinal fundus images. This article introduces a Deep Learning Enabled Large Scale Healthcare Decision Making for Diabetic Retinopathy (DLLSHDM-DR) on Retinal Fundus Images. The proposed DLLSHDM-DR technique intends to assist physicians with the DR decision-making method. In the DLLSHDM-DR technique, image preprocessing is initially… More >

Nora Abdullah Alkhaldi^1,*, Hanan T. Halawani²

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 399-414, 2023, DOI:10.32604/cmc.2023.030872

Abstract Automated segmentation of blood vessels in retinal fundus images is essential for medical image analysis. The segmentation of retinal vessels is assumed to be essential to the progress of the decision support system for initial analysis and treatment of retinal disease. This article develops a new Grasshopper Optimization with Fuzzy Edge Detection based Retinal Blood Vessel Segmentation and Classification (GOFED-RBVSC) model. The proposed GOFED-RBVSC model initially employs contrast enhancement process. Besides, GOAFED approach is employed to detect the edges in the retinal fundus images in which the use of GOA adjusts the membership functions. The ORB (Oriented FAST and Rotated… More >

M. P. Karthikeyan^1,*, E. A. Mary Anita²

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 567-580, 2023, DOI:10.32604/iasc.2023.026243

Abstract In recent years, there has been a significant increase in the number of people suffering from eye illnesses, which should be treated as soon as possible in order to avoid blindness. Retinal Fundus images are employed for this purpose, as well as for analysing eye abnormalities and diagnosing eye illnesses. Exudates can be recognised as bright lesions in fundus pictures, which can be the first indicator of diabetic retinopathy. With that in mind, the purpose of this work is to create an Integrated Model for Exudate and Diabetic Retinopathy Diagnosis (IM-EDRD) with multi-level classifications. The model uses Support Vector Machine… More >

Qaisar Abbas^1,*, Mostafa E. A. Ibrahim^1,2, 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 >

Muhammad Shujaat¹, Numan Aslam¹, Iram Noreen¹, Muhammad Khurram Ehsan^1,*, 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 >