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

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.