Indian Diabetic Retinopathy Image Dataset (IDRiD): A Database for Diabetic Retinopathy Screening Research

Prasanna Porwal, Samiksha Pachade, Ravi Kamble, Manesh Kokare, Girish Deshmukh, Vivek Sahasrabuddhe and Fabrice Meriaudeau
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Prasanna Porwal: Center of Excellence in Signal and Image Processing, Department of Electronics and Telecommunication Engineering, Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded 431606, India
Samiksha Pachade: Center of Excellence in Signal and Image Processing, Department of Electronics and Telecommunication Engineering, Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded 431606, India
Ravi Kamble: Center of Excellence in Signal and Image Processing, Department of Electronics and Telecommunication Engineering, Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded 431606, India
Manesh Kokare: Center of Excellence in Signal and Image Processing, Department of Electronics and Telecommunication Engineering, Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded 431606, India
Girish Deshmukh: Eye Clinic, Sushrusha Hospital, Nanded 431601, India
Vivek Sahasrabuddhe: Department of Ophthalmology, Shankarrao Chavan Government Medical College, Nanded 431606, India
Fabrice Meriaudeau: Centre for Intelligent Signal and Imaging Research, Department of Electrical & Electronic Engineering, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Malaysia

Data, 2018, vol. 3, issue 3, 1-8

Abstract: Diabetic Retinopathy is the most prevalent cause of avoidable vision impairment, mainly affecting the working-age population in the world. Recent research has given a better understanding of the requirement in clinical eye care practice to identify better and cheaper ways of identification, management, diagnosis and treatment of retinal disease. The importance of diabetic retinopathy screening programs and difficulty in achieving reliable early diagnosis of diabetic retinopathy at a reasonable cost needs attention to develop computer-aided diagnosis tool. Computer-aided disease diagnosis in retinal image analysis could ease mass screening of populations with diabetes mellitus and help clinicians in utilizing their time more efficiently. The recent technological advances in computing power, communication systems, and machine learning techniques provide opportunities to the biomedical engineers and computer scientists to meet the requirements of clinical practice. Diverse and representative retinal image sets are essential for developing and testing digital screening programs and the automated algorithms at their core. To the best of our knowledge, IDRiD (Indian Diabetic Retinopathy Image Dataset), is the first database representative of an Indian population. It constitutes typical diabetic retinopathy lesions and normal retinal structures annotated at a pixel level. The dataset provides information on the disease severity of diabetic retinopathy, and diabetic macular edema for each image. This makes it perfect for development and evaluation of image analysis algorithms for early detection of diabetic retinopathy.

Keywords: retinal fundus images; diabetic retinopathy; diabetic macular edema (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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