LISAC Laboratory, Department of Informatics, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco.
LISAC Laboratory, Department of Informatics, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco.
Comput Biol Med. 2024 Jun;175:108523. doi: 10.1016/j.compbiomed.2024.108523. Epub 2024 Apr 25.
Diabetic retinopathy is considered one of the most common diseases that can lead to blindness in the working age, and the chance of developing it increases as long as a person suffers from diabetes. Protecting the sight of the patient or decelerating the evolution of this disease depends on its early detection as well as identifying the exact levels of this pathology, which is done manually by ophthalmologists. This manual process is very consuming in terms of the time and experience of an expert ophthalmologist, which makes developing an automated method to aid in the diagnosis of diabetic retinopathy an essential and urgent need. In this paper, we aim to propose a new hybrid deep learning method based on a fine-tuning vision transformer and a modified capsule network for automatic diabetic retinopathy severity level prediction. The proposed approach consists of a new range of computer vision operations, including the power law transformation technique and the contrast-limiting adaptive histogram equalization technique in the preprocessing step. While the classification step builds up on a fine-tuning vision transformer, a modified capsule network, and a classification model combined with a classification model, The effectiveness of our approach was evaluated using four datasets, including the APTOS, Messidor-2, DDR, and EyePACS datasets, for the task of severity levels of diabetic retinopathy. We have attained excellent test accuracy scores on the four datasets, respectively: 88.18%, 87.78%, 80.36%, and 78.64%. Comparing our results with the state-of-the-art, we reached a better performance.
糖尿病视网膜病变被认为是最常见的导致工作年龄人群失明的疾病之一，只要患者患有糖尿病，其发病的可能性就会增加。保护患者的视力或减缓这种疾病的发展取决于早期发现以及确定这种病理的确切程度，这是由眼科医生手动完成的。这个手动过程非常耗费专家眼科医生的时间和经验，这使得开发一种自动化方法来辅助糖尿病视网膜病变的诊断成为一种必要和紧迫的需求。在本文中，我们旨在提出一种新的混合深度学习方法，该方法基于微调的视觉转换器和改进的胶囊网络，用于自动预测糖尿病视网膜病变的严重程度等级。所提出的方法包括一系列新的计算机视觉操作，包括预处理步骤中的幂律变换技术和对比度限制自适应直方图均衡化技术。而分类步骤则基于微调的视觉转换器、改进的胶囊网络以及与分类模型相结合的分类模型构建。我们使用了四个数据集（APTOS、Messidor-2、DDR 和 EyePACS 数据集）来评估我们的方法在糖尿病视网膜病变严重程度等级预测任务上的有效性。我们在四个数据集上分别获得了优异的测试准确率得分：88.18%、87.78%、80.36%和 78.64%。与最先进的技术相比，我们的结果表现更好。
