Improved Detection of Fundus Lesions Using YOLOR-CSP Architecture and Slicing Aided Hyper Inference

Authors

Keywords:

diabetes mellitus, diabetic retinopathy, fundus image, lesions detection, deep learning

Abstract

Diabetes affects millions of people worldwide, and diabetic retinopathy is a complication of diabetes. Brazil is the sixth in the world in the incidence of diabetes and has the highest numbers in Latin America with 15.7 million adults affected by this condition. Typically diabetic retinopathy is identified by lesions such as hard and soft exudates, microaneurysms, and vitreous hemorrhages. Early diagnosis of these lesions is essential to prevent the progression of the disease to severe stages and the consequent loss of vision. As the disease diagnosis is based on image analysis, it is possible to use deep learning models to detect artifacts in the retina. This article proposes a new method that uses a YOLOR-CSP architecture combined with the Slicing Aided Hyper Inference framework to detect fundus lesions. The proposed method was trained, adjusted, and evaluated using the DDR dataset, obtaining a mAP equal to 38.08%. The proposed method achieved values of AP equal to 40.90%, 46.60%, 26.10%, and 38.70% for hard exudates, soft exudates, microaneurysms, and vitreous hemorrhages, respectively, surpassing similar studies found in the literature.

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Author Biographies

Alejandro Pereira, Universidade Federal de Pelotas (UFPEL)

Undergraduate student in Computer Science at the Federal University of Pelotas (UFPEL). From 2021 to 2022 he participated in scientific initiation programs funded by CNPq, working on the design of cyber-physical systems for pest management. His research has focused on the areas of Artificial Intelligence, especially using computer vision to solve health problems, and also in the area of wireless sensor networks applied to pest management.

Carlos Santos, Instituto Federal de Educação, Ciência e Tecnologia Farroupilha (IFFAR)

Ph.D. candidate at the Postgraduate Program in Computing, Federal University of Pelotas (UFPEL). Received the B.Sc. in Informatics from University of the Region of Campanha (URCAMP), in 2010 and his M.Sc. in Electrical Engineering from the Federal University of Pampa (UNIPAMPA), in 2017. Currently, he is a Professor at Federal Institute of Education, Science and Technology Farroupilha (IFFAR). His corrent research interests include Applications of Artificial Intelligence in Diagnostic Imaging, Computer Aided Detection (CAD) and Computer Aided Diagnosis (CADx).

Marilton Aguiar, Universidade Federal de Pelotas (UFPEL)

Associate Professor at the Federal University of Pelotas (UFPEL) in the scope of the Undergraduate Courses in Computer Science and Engineering and the Graduate Program in Computing. Director of the Center for Technological Development at the Federal University of Pelotas. His research has focused on applications of Artificial Intelligence in solving environmental and health problems.

Daniel Welfer, Universidade Federal de Santa Maria (UFSM)

Professor of the Department of Applied Computing of the Federal University of Santa Maria (UFSM). Currently, he is also a permanent professor at the Graduate Program in Computer Science (PPGCC). Specializing in the fields of image processing, artificial intelligence and software engineering.

Marcelo Dias, Universidade Federal de Pelotas (UFPEL)

Undergraduate student of Computer Science at Federal University of Pelotas (UFPEL). He has participated in undergraduate research programs (PROBIC/FAPERGS) funded by government agencies. His research interests are in the areas of Artificial Inteligence, using image detection to diagnostic.

Marcelo Ribeiro, Universidade Federal de Pelotas (UFPEL)

Undergraduate student of Computer Science at the Federal University of Pelotas (UFPEL). His research interests are in the areas of Artificial Intelligence, with focus on object detection and synthetic data generation.

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Published

2023-07-24

How to Cite

Pereira, A., Santos, C., Aguiar, M., Welfer, D., Dias, M., & Ribeiro, M. (2023). Improved Detection of Fundus Lesions Using YOLOR-CSP Architecture and Slicing Aided Hyper Inference. IEEE Latin America Transactions, 21(7), 806–813. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/7792