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In recent years, there has been an increasing interest among deep learning researchers in developing automated neoplasm detection systems to serve as assistive tools for clinicians in detecting diminutive and inconspicuous polyps. Precise segmentation of these neoplasms in medical images is essential for early detection and intervention. While current research efforts focus on enhancing segmentation performance and achieving new state-of-the-art results, a comprehensive analysis of the various factors influencing the performance of neoplasm segmentation models remains to be conducted. In this study, we investigate the impact of color space on the performance of colorectal neoplasm segmentation networks. We employ three pre-trained semantic segmentation architectures: U-NET, DeepLabV3 and Pyramid Attention Network (PAN) to elucidate the relationship between the color space of input images and model performance. We examine this relationship using four color spaces: 1) RGB, 2) HSV, 3) HSL and 4) CIEL*A*B*. Four publicly available datasets: Kvasir-SEG CVC-ClinicDB, CVC-ColonDB and ETIS-LaribPolypDB, are utilized for training and testing. Our findings indicate that the choice of color space can significantly influence the performance of colorectal neoplasm segmentation networks.<\/jats:p>","DOI":"10.1177\/18724981241297459","type":"journal-article","created":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T09:51:32Z","timestamp":1747734692000},"page":"980-1006","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Color space and colorectal polyps segmentation: An empirical study"],"prefix":"10.1177","volume":"19","author":[{"given":"Khaled","family":"ELKarazle","sequence":"first","affiliation":[{"name":"Faculty of Engineering, Computing, and Science, Swinburne University of Technology Sarawak Campus, Kuching, Sarawak 93350, Malaysia"}]},{"given":"Valliappan","family":"Raman","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Data Science, Coimbatore Institute of Technology, Coimbatore, Tamil Nadu, 641014, India"}]},{"given":"Caslon","family":"Chua","sequence":"additional","affiliation":[{"name":"Faculty of Science, Engineering and Technology, Swinburne University of Technology, Melbourne, VIC 3122, Australia"}]},{"given":"Patrick","family":"Then","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Computing, and Science, Swinburne University of Technology Sarawak Campus, Kuching, Sarawak 93350, Malaysia"}]}],"member":"179","published-online":{"date-parts":[[2024,12,5]]},"reference":[{"key":"e_1_3_4_2_2","doi-asserted-by":"publisher","DOI":"10.3121\/cmr.1.3.261"},{"key":"e_1_3_4_3_2","doi-asserted-by":"publisher","DOI":"10.1136\/gut.39.3.449"},{"key":"e_1_3_4_4_2","doi-asserted-by":"publisher","DOI":"10.1097\/MOG.0000000000000988"},{"key":"e_1_3_4_5_2","unstructured":"Colon Cancer: Diagnosis and Staging | Johns Hopkins Medicine. https:\/\/www.hopkinsmedicine.org\/health\/conditions-and-diseases\/colon-cancer\/colon-cancer-diagnosis-and-staging (accessed March 9 2024). n.d."},{"key":"e_1_3_4_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(19)32319-0"},{"key":"e_1_3_4_7_2","unstructured":"World Health Organization Colorectal Cancer. https:\/\/www.iarc.who.int\/cancer-type\/colorectal-cancer\/ (accessed December 20 2022). 2022."},{"key":"e_1_3_4_8_2","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics12030747"},{"key":"e_1_3_4_9_2","doi-asserted-by":"publisher","DOI":"10.1038\/nrgastro.2011.141"},{"key":"e_1_3_4_10_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12876-023-02981-3"},{"key":"e_1_3_4_11_2","article-title":"Colonoscopy quality across Europe: a report of the European colonoscopy quality investigation (ECQI) group","volume":"09","author":"Spada C","year":"2021","unstructured":"Spada C, Koulaouzidis A, Hassan C, et\u00a0al. 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