Binary and multi-class automated detection of age-related macular degeneration using convolutional- and transformer-based architectures. (February 2023)

• Record Type: Journal Article
• Authors: Domínguez, César; Heras, Jónathan; Mata, Eloy; Pascual, Vico; Royo, Didac; Zapata, Miguel Ángel
• Is Part Of: Computer methods and programs in biomedicine. Volume 229(2023)
• Publication Date: 2023-02
• Subjects: Age-related macular degeneration -- Deep learning -- Transformers -- Convolutional neural networks -- Ensembles; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.107302
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Age-related macular degeneration (AMD) is the leading cause of blindness in developed countries. We have studied convolutional- and transformer-based architectures for diagnosing and grading AMD. Convolutional-based architectures yielded better results than transformer-based models. Convolutional-based architectures can be boosted by progressive resizing schemes and ensemble methods. We have built models for diagnosing referable AMD and grading AMD with a mean weighted kappa (SD) over 84%. Abstract: Background and Objective: Age-related macular degeneration (AMD) is an eye disease that happens when ageing causes damage to the macula, and it is the leading cause of blindness in developed countries. Screening retinal fundus images allows ophthalmologists to early detect, diagnose and treat this disease; however, the manual interpretation of images is a time-consuming task. In this paper, we aim to study different deep learning methods to diagnose AMD. Methods: We have conducted a thorough study of two families of deep learning models based on convolutional neural networks (CNN) and transformer architectures to automatically diagnose referable/non-referable AMD, and grade AMD severity scales (no AMD, early AMD, intermediate AMD, and advanced AMD). In addition, we have analysed several progressive resizing strategies and ensemble methods for convolutional-based architectures to further improve the performance of the models. Results: As a first result, we have shown that transformer-based architectures obtain considerably worse results than convolutional-based architectures for diagnosing AMD. Moreover, we have built a model for diagnosing referable AMD that yielded a mean F1-score (SD) of 92.60% (0.47), a mean AUROC (SD) of 97.53% (0.40), and a mean weighted kappa coefficient (SD) of 85.28% (0.91); and an ensemble of models for grading AMD severity scales with a mean accuracy (SD) of 82.55% (2.92), and a mean weighted kappa coefficient (SD) of 84.76% (2.45). Conclusions: This work shows that working with convolutional based architectures is more suitable than using transformer based models for classifying and grading AMD from retinal fundus images. Furthermore, convolutional models can be improved by means of progressive resizing strategies and ensemble methods.