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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Sachin Panchal & Ankita Naik & Manesh Kokare & Samiksha Pachade & Rushikesh Naigaonkar & Prerana Phadnis & Archana Bhange, 2023. "Retinal Fundus Multi-Disease Image Dataset (RFMiD) 2.0: A Dataset of Frequently and Rarely Identified Diseases," Data, MDPI, vol. 8(2), pages 1-16, January.
    • Handle: RePEc:gam:jdataj:v:8:y:2023:i:2:p:29-:d:1049386
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    References listed on IDEAS

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    1. Ling-Ping Cen & Jie Ji & Jian-Wei Lin & Si-Tong Ju & Hong-Jie Lin & Tai-Ping Li & Yun Wang & Jian-Feng Yang & Yu-Fen Liu & Shaoying Tan & Li Tan & Dongjie Li & Yifan Wang & Dezhi Zheng & Yongqun Xiong, 2021. "Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
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