Deep Learning-Based Colorectal Cancer Image Segmentation and Classification: A Concise Bibliometric Analysis

Fagbola, Temitayo Matthew; Aderemi, Emmanuel Tunbosun; Thakur, Colin Surendra

Abstract

The use of Deep Learning (DL)-based methods for Colorectal Cancer (CRC) classification and segmentation has gained significant attention in recent times. This study employs a bibliometric analysis to investigate the state-of-The-art research on DL-based CRC image analysis. The analysis aims to provide quantitative insights into publication trends, collaboration patterns, influential publications and knowledge gaps within the DL-based CRC image analysis. We conducted a search in the Scopus database for articles published between 2017 and 2022 using keywords related to DL, CRC image classification and segmentation. Only research articles applying DL methods for these tasks were included. VOSviewer software was used to analyze the retrieved data. The results show that there is a relatively low number of articles in this area, highlighting the need for more research. Collaboration between authors, universities, and countries needs to be improved to further advance research in this direction. The findings of this study can help researchers identify gaps and opportunities for further intervention in CRC research as guide towards future research. Furthermore, the potential of DL-based methods to reduce human effort and enhance CRC diagnosis and treatment is emphasized.

Citation

Fagbola, T. M., Aderemi, E. T., & Thakur, C. S. (2024, August). Deep Learning-Based Colorectal Cancer Image Segmentation and Classification: A Concise Bibliometric Analysis. Presented at 7th International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD), Mauritius

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 7th International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD)
• Start Date: Aug 1, 2024
• End Date: Aug 2, 2024
• Acceptance Date: May 10, 2024
• Online Publication Date: Aug 29, 2024
• Publication Date: Aug 29, 2024
• Deposit Date: Jun 21, 2024
• Publicly Available Date: Aug 30, 2026
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Book Title: 2024 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD)
• ISBN: 979-8-3503-8790-2
• DOI: https://doi.org/10.1109/icABCD62167.2024.10645233
• Keywords: colorectal cancer; bibliometric analysis; image classification; image segmentation; deep learning
• Public URL: https://hull-repository.worktribe.com/output/4716139
• Publisher URL: https://ieeexplore.ieee.org/document/10645233