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Open-Source Datasets for Colonoscopy Polyps and Its AI-Enabled Techniques

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Communication and Intelligent Systems (ICCIS 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 686))

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Abstract

High-quality, open access, and free colonoscopy data act as a catalyst for the ongoing state-of-the-art (SOTA) artificial intelligence (AI) research works in the detection, localization, segmentation, and classification of various colorectal cancer findings in the colon and rectum such as polyp growth, abnormal tissues, cancerous and non-cancerous lesions, etc. This paper presents and summarizes widely used, open-source, and downloadable polyp colonoscopy datasets along with its source links. A comparative analysis of twenty-one colonoscopy datasets has been done. Copy-right-free, raw colonoscopy data (videos and images) from various medical Websites and YouTube videos are also mentioned for potential data development. Such colonoscopy datasets will further aid in the development of AI-powered software and hardware medical systems in this field and eventually help in reducing the burden of experienced gastroenterologists.

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Authors and Affiliations

  1. Department of ECE, IGDTUW, Delhi, India

    Harshita Mangotra & Nidhi Gooel

  2. Department of ECE, DTU, Delhi, India

    Palak Handa

Authors
  1. Harshita Mangotra
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  2. Palak Handa
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  3. Nidhi Gooel
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Corresponding author

Correspondence to Nidhi Gooel .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, Rajasthan Technical University, Kota, India

    Harish Sharma

  2. National Institute of Technology Delhi, New Delhi, Delhi, India

    Vivek Shrivastava

  3. Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, Madhya Pradesh, India

    Kusum Kumari Bharti

  4. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Lipo Wang

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Mangotra, H., Handa, P., Gooel, N. (2023). Open-Source Datasets for Colonoscopy Polyps and Its AI-Enabled Techniques. In: Sharma, H., Shrivastava, V., Bharti, K.K., Wang, L. (eds) Communication and Intelligent Systems. ICCIS 2022. Lecture Notes in Networks and Systems, vol 686. Springer, Singapore. https://doi.org/10.1007/978-981-99-2100-3_6

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