A new ultra-wide-field fundus dataset to diabetic retinopathy grading using hybrid preprocessing methods. (May 2023)

• Record Type: Journal Article
• Authors: Liu, Haomiao; Teng, Lu; Fan, Linhua; Sun, Yabin; Li, Huiying
• Is Part Of: Computers in biology and medicine. Volume 157(2023)
• Publication Date: 2023-05
• Subjects: Diabetic retinopathy grading -- Ultra-wide-field -- Deep learning -- Convolutional neural network -- Vision transformer; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2023.106750
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Diabetic retinopathy(DR) is a common early diabetic complication and one of the main causes of blindness. In clinical diagnosis and treatment, regular screening with fundus imaging is an effective way to prevent the development of DR. However, the regular fundus images used in most DR screening work have a small imaging range, narrow field of vision, and can not contain more complete lesion information, which leads to less ideal automatic DR grading results. In order to improve the accuracy of DR grading, we establish a dataset containing 101 ultra-wide-field(UWF) DR fundus images and propose a deep learning(DL) automatic classification method based on a new preprocessing method. The emerging UWF fundus images have the advantages of a large imaging range and wide field of vision and contain more information about the lesions. In data preprocessing, we design a data denoising method for UWF images and use data enhancement methods to improve their contrast and brightness to improve the classification effect. In order to verify the efficiency of our dataset and the effectiveness of our preprocessing method, we design a series of experiments including a variety of DL classification models. The experimental results show that we can achieve high classification accuracy by using only the backbone model. The most basic ResNet50 model reaches an average of classification accuracy(ACA) 0.66, Macro F1 0.6559, and Kappa 0.58. The best-performing Swin-S model reaches ACA 0.72, Macro F1 0.7018, and Kappa 0.65. DR grading using UWF images can achieve higher accuracy and efficiency, which has practical significance and value in clinical applications. Highlights: An ultra-wide-field(UWF) diabetic retinopathy (DR) fundus dataset containing 101 images is established. Various image enhancement methods are discussed and tested. A novel preprocessing method is proposed for UWF DR grading. Various popular deep learning models are deployed and the backbone model can get good performance.