Worapan Kusakunniran, Sarattha Karnjanapreechakorn, Pitipol Choopong, T. Siriapisith, N. Tesavibul, N. Phasukkijwatana, S. Prakhunhungsit, Sutasinee Boonsopon
{"title":"多分支CNN在视网膜图像中检测和分期糖尿病视网膜病变","authors":"Worapan Kusakunniran, Sarattha Karnjanapreechakorn, Pitipol Choopong, T. Siriapisith, N. Tesavibul, N. Phasukkijwatana, S. Prakhunhungsit, Sutasinee Boonsopon","doi":"10.1108/aci-06-2022-0150","DOIUrl":null,"url":null,"abstract":"PurposeThis paper aims to propose a solution for detecting and grading diabetic retinopathy (DR) in retinal images using a convolutional neural network (CNN)-based approach. It could classify input retinal images into a normal class or an abnormal class, which would be further split into four stages of abnormalities automatically.Design/methodology/approachThe proposed solution is developed based on a newly proposed CNN architecture, namely, DeepRoot. It consists of one main branch, which is connected by two side branches. The main branch is responsible for the primary feature extractor of both high-level and low-level features of retinal images. Then, the side branches further extract more complex and detailed features from the features outputted from the main branch. They are designed to capture details of small traces of DR in retinal images, using modified zoom-in/zoom-out and attention layers.FindingsThe proposed method is trained, validated and tested on the Kaggle dataset. The regularization of the trained model is evaluated using unseen data samples, which were self-collected from a real scenario from a hospital. It achieves a promising performance with a sensitivity of 98.18% under the two classes scenario.Originality/valueThe new CNN-based architecture (i.e. DeepRoot) is introduced with the concept of a multi-branch network. It could assist in solving a problem of an unbalanced dataset, especially when there are common characteristics across different classes (i.e. four stages of DR). Different classes could be outputted at different depths of the network.","PeriodicalId":37348,"journal":{"name":"Applied Computing and Informatics","volume":null,"pages":null},"PeriodicalIF":12.3000,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Detecting and staging diabetic retinopathy in retinal images using multi-branch CNN\",\"authors\":\"Worapan Kusakunniran, Sarattha Karnjanapreechakorn, Pitipol Choopong, T. Siriapisith, N. Tesavibul, N. Phasukkijwatana, S. Prakhunhungsit, Sutasinee Boonsopon\",\"doi\":\"10.1108/aci-06-2022-0150\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"PurposeThis paper aims to propose a solution for detecting and grading diabetic retinopathy (DR) in retinal images using a convolutional neural network (CNN)-based approach. It could classify input retinal images into a normal class or an abnormal class, which would be further split into four stages of abnormalities automatically.Design/methodology/approachThe proposed solution is developed based on a newly proposed CNN architecture, namely, DeepRoot. It consists of one main branch, which is connected by two side branches. The main branch is responsible for the primary feature extractor of both high-level and low-level features of retinal images. Then, the side branches further extract more complex and detailed features from the features outputted from the main branch. They are designed to capture details of small traces of DR in retinal images, using modified zoom-in/zoom-out and attention layers.FindingsThe proposed method is trained, validated and tested on the Kaggle dataset. The regularization of the trained model is evaluated using unseen data samples, which were self-collected from a real scenario from a hospital. It achieves a promising performance with a sensitivity of 98.18% under the two classes scenario.Originality/valueThe new CNN-based architecture (i.e. DeepRoot) is introduced with the concept of a multi-branch network. It could assist in solving a problem of an unbalanced dataset, especially when there are common characteristics across different classes (i.e. four stages of DR). 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Detecting and staging diabetic retinopathy in retinal images using multi-branch CNN
PurposeThis paper aims to propose a solution for detecting and grading diabetic retinopathy (DR) in retinal images using a convolutional neural network (CNN)-based approach. It could classify input retinal images into a normal class or an abnormal class, which would be further split into four stages of abnormalities automatically.Design/methodology/approachThe proposed solution is developed based on a newly proposed CNN architecture, namely, DeepRoot. It consists of one main branch, which is connected by two side branches. The main branch is responsible for the primary feature extractor of both high-level and low-level features of retinal images. Then, the side branches further extract more complex and detailed features from the features outputted from the main branch. They are designed to capture details of small traces of DR in retinal images, using modified zoom-in/zoom-out and attention layers.FindingsThe proposed method is trained, validated and tested on the Kaggle dataset. The regularization of the trained model is evaluated using unseen data samples, which were self-collected from a real scenario from a hospital. It achieves a promising performance with a sensitivity of 98.18% under the two classes scenario.Originality/valueThe new CNN-based architecture (i.e. DeepRoot) is introduced with the concept of a multi-branch network. It could assist in solving a problem of an unbalanced dataset, especially when there are common characteristics across different classes (i.e. four stages of DR). Different classes could be outputted at different depths of the network.
期刊介绍:
Applied Computing and Informatics aims to be timely in disseminating leading-edge knowledge to researchers, practitioners and academics whose interest is in the latest developments in applied computing and information systems concepts, strategies, practices, tools and technologies. In particular, the journal encourages research studies that have significant contributions to make to the continuous development and improvement of IT practices in the Kingdom of Saudi Arabia and other countries. By doing so, the journal attempts to bridge the gap between the academic and industrial community, and therefore, welcomes theoretically grounded, methodologically sound research studies that address various IT-related problems and innovations of an applied nature. The journal will serve as a forum for practitioners, researchers, managers and IT policy makers to share their knowledge and experience in the design, development, implementation, management and evaluation of various IT applications. Contributions may deal with, but are not limited to: • Internet and E-Commerce Architecture, Infrastructure, Models, Deployment Strategies and Methodologies. • E-Business and E-Government Adoption. • Mobile Commerce and their Applications. • Applied Telecommunication Networks. • Software Engineering Approaches, Methodologies, Techniques, and Tools. • Applied Data Mining and Warehousing. • Information Strategic Planning and Recourse Management. • Applied Wireless Computing. • Enterprise Resource Planning Systems. • IT Education. • Societal, Cultural, and Ethical Issues of IT. • Policy, Legal and Global Issues of IT. • Enterprise Database Technology.