Retinal Fundus Multi-Disease Image Dataset (RFMiD) 2.0: A Dataset of Frequently and Rarely Identified Diseases

Sachin Panchal (), Ankita Naik, Manesh Kokare (), Samiksha Pachade, Rushikesh Naigaonkar, Prerana Phadnis and Archana Bhange
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Sachin Panchal: Center of Excellence in Signal and Image Processing, Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded 431606, Maharashtra, India
Ankita Naik: Center of Excellence in Signal and Image Processing, Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded 431606, Maharashtra, India
Manesh Kokare: Center of Excellence in Signal and Image Processing, Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded 431606, Maharashtra, India
Samiksha Pachade: School of Biomedical Informatics, The University of Texas Health Science Center, 7000 Fannin St Suite 600, Houston, TX 77030, USA
Rushikesh Naigaonkar: Shri Ganapati Netralaya State of Art Eye Care Hospital, Jalna 431203, Maharashtra, India
Prerana Phadnis: Lions Eye Hospital, Nanded 431603, Maharashtra, India
Archana Bhange: Keya Eye Clinic, Pune 411062, Maharashtra, India

Data, 2023, vol. 8, issue 2, 1-16

Abstract: Irreversible vision loss is a worldwide threat. Developing a computer-aided diagnosis system to detect retinal fundus diseases is extremely useful and serviceable to ophthalmologists. Early detection, diagnosis, and correct treatment could save the eye’s vision. Nevertheless, an eye may be afflicted with several diseases if proper care is not taken. A single retinal fundus image might be linked to one or more diseases. Age-related macular degeneration, cataracts, diabetic retinopathy, Glaucoma, and uncorrected refractive errors are the leading causes of visual impairment. Our research team at the center of excellence lab has generated a new dataset called the Retinal Fundus Multi-Disease Image Dataset 2.0 (RFMiD2.0). This dataset includes around 860 retinal fundus images, annotated by three eye specialists, and is a multiclass, multilabel dataset. We gathered images from a research facility in Jalna and Nanded, where patients across Maharashtra come for preventative and therapeutic eye care. Our dataset would be the second publicly available dataset consisting of the most frequent diseases, along with some rarely identified diseases. This dataset is auxiliary to the previously published RFMiD dataset. This dataset would be significant for the research and development of artificial intelligence in ophthalmology.

Keywords: data analysis; ocular diseases; retinal fundus image dataset; data annotation; multilabel classification (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2023
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