Retinal Fundus Multi-Disease Image Dataset (RFMiD): A Dataset for Multi-Disease Detection Research

Samiksha Pachade, Prasanna Porwal, Dhanshree Thulkar, Manesh Kokare, Girish Deshmukh, Vivek Sahasrabuddhe, Luca Giancardo, Gwenolé Quellec and Fabrice Mériaudeau
Additional contact information
Samiksha Pachade: Center of Excellence in Signal and Image Processing, Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded 431606, India
Prasanna Porwal: Center of Excellence in Signal and Image Processing, Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded 431606, India
Dhanshree Thulkar: Electronics Department, Veermata Jijabai Technological Institute, Mumbai 400019, India
Manesh Kokare: Center of Excellence in Signal and Image Processing, 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
Luca Giancardo: Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center at Houston (UTHealth), Houston, TX 77030, USA
Gwenolé Quellec: Inserm, UMR 1101, F-29200 Brest, France
Fabrice Mériaudeau: ImViA EA 7535 and ERL VIBOT 6000, Université de Bourgogne, 21078 Dijon, France

Data, 2021, vol. 6, issue 2, 1-14

Abstract: The world faces difficulties in terms of eye care, including treatment, quality of prevention, vision rehabilitation services, and scarcity of trained eye care experts. Early detection and diagnosis of ocular pathologies would enable forestall of visual impairment. One challenge that limits the adoption of computer-aided diagnosis tool by ophthalmologists is the number of sight-threatening rare pathologies, such as central retinal artery occlusion or anterior ischemic optic neuropathy, and others are usually ignored. In the past two decades, many publicly available datasets of color fundus images have been collected with a primary focus on diabetic retinopathy, glaucoma, age-related macular degeneration and few other frequent pathologies. To enable development of methods for automatic ocular disease classification of frequent diseases along with the rare pathologies, we have created a new Retinal Fundus Multi-disease Image Dataset (RFMiD). It consists of 3200 fundus images captured using three different fundus cameras with 46 conditions annotated through adjudicated consensus of two senior retinal experts. To the best of our knowledge, our dataset, RFMiD, is the only publicly available dataset that constitutes such a wide variety of diseases that appear in routine clinical settings. This dataset will enable the development of generalizable models for retinal screening.

Keywords: retinal fundus images; rare pathology detection; ocular disease; classification; multi-label classification (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2306-5729/6/2/14/pdf (application/pdf)
https://www.mdpi.com/2306-5729/6/2/14/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jdataj:v:6:y:2021:i:2:p:14-:d:492119

Access Statistics for this article

Data is currently edited by Ms. Cecilia Yang

More articles in Data from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().