Aqsa Aslam, Saima Farhan^*, Momina Abdul Khaliq, Fatima Anjum, Ayesha Afzaal, Faria Kanwal

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2607-2622, 2023, DOI:10.32604/iasc.2023.034041

Abstract Use of deep learning algorithms for the investigation and analysis of medical images has emerged as a powerful technique. The increase in retinal diseases is alarming as it may lead to permanent blindness if left untreated. Automation of the diagnosis process of retinal diseases not only assists ophthalmologists in correct decision-making but saves time also. Several researchers have worked on automated retinal disease classification but restricted either to hand-crafted feature selection or binary classification. This paper presents a deep learning-based approach for the automated classification of multiple retinal diseases using fundus images. For this research, the data has been collected… More >

Thavavel Vaiyapuri¹, S. Srinivasan², Mohamed Yacin Sikkandar³, T. S. Balaji^4,5, Seifedine Kadry⁶, Maytham N. Meqdad⁷, Yunyoung Nam^8,*

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5543-5557, 2022, DOI:10.32604/cmc.2022.023919

Abstract In past decades, retinal diseases have become more common and affect people of all age grounds over the globe. For examining retinal eye disease, an artificial intelligence (AI) based multilabel classification model is needed for automated diagnosis. To analyze the retinal malady, the system proposes a multiclass and multi-label arrangement method. Therefore, the classification frameworks based on features are explicitly described by ophthalmologists under the application of domain knowledge, which tends to be time-consuming, vulnerable generalization ability, and unfeasible in massive datasets. Therefore, the automated diagnosis of multi-retinal diseases becomes essential, which can be solved by the deep learning (DL)… More >

Kanupriya Mittal^*, V. Mary Anita Rajam

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1907-1921, 2023, DOI:10.32604/iasc.2023.029037

Abstract An automated retinal disease detection system has long been in existence and it provides a safe, no-contact and cost-effective solution for detecting this disease. This paper presents a game theory-based dynamic weighted ensemble of a feature extraction-based machine learning model and a deep transfer learning model for automatic retinal disease detection. The feature extraction-based machine learning model uses Gaussian kernel-based fuzzy rough sets for reduction of features, and XGBoost classifier for the classification. The transfer learning model uses VGG16 or ResNet50 or Inception-ResNet-v2. A novel ensemble classifier based on the game theory approach is proposed for the fusion of the… More >

Kuntha Pin¹, Jee Ho Chang², Yunyoung Nam^3,*

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 >