Automatic arteriosclerotic retinopathy grading using four-channel with image merging. (September 2021)

• Record Type: Journal Article
• Authors: Gao, Shuo; Gao, Li; Quan, Xiongwen; Zhang, Han; Bai, Hang; Kang, Chuanze
• Is Part Of: Computer methods and programs in biomedicine. Volume 208(2021)
• Publication Date: 2021-09
• Subjects: Arteriosclerotic retinopathy grading -- Contour channel -- Image merge -- ArcLossdeep -- convolutional neural network; 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.2021.106274
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: To our best knowledge, we first propose the new grading task for arteriosclerotic retinopathy Adaptive threshold processing is applied to image to generate the new contour channel, which merges with the original three-channel image. We employ the pre-trained convolutional neural network with transfer learning and contour image channel parameter with Kaiming initialization. Additive Angular Margin Loss is employed to obtain highly discriminative features. Abstract: Background and objective: Arteriosclerosis can reflect the severity of hypertension, which is one of the main diseases threatening human life safety. But Arteriosclerosis retinopathy detection involves costly and time-consuming manual assessment. To meet the urgent needs of automation, this paper developed a novel arteriosclerosis retinopathy grading method based on convolutional neural network. Methods: Firstly, we propose a good scheme for extracting features facing the fundus blood vessel background using image merging for contour enhancement. In this step, the original image is dealt with adaptive threshold processing to generate the new contour channel, which merge with the original three-channel image. Then, we employ the pre-trained convolutional neural network with transfer learning to speed up training and contour image channel parameter with Kaiming initialization. Moreover, ArcLoss is applied to increase inter-class differences and intra-class similarity aiming to the high similarity of images of different classes in the dataset. Results: The accuracy of arteriosclerosis retinopathy grading achieved by the proposed method is up to 65.354%, which is nearly 4% higher than those of the exiting methods. The Kappa of our method is 0.508 in arteriosclerosis retinopathy grading. Conclusions: An experimental study on multiple metrics demonstrates the superiority of our method, which will be a useful to the toolbox for arteriosclerosis retinopathy grading.