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A key step in cancer treatment is the identification and removal of tumorous tissue, which is subsequently analyzed through pathological examinations. However, traditional pathology reports can take several weeks or even months to be processed. To expedite diagnosis, this study employs microwave measurement techniques to distinguish between healthy and cancerous colon tissue samples. The free\u2010space measurement method is selected due to its suitability for evaluating sensitive pathological tissues. Measurements are conducted across 201 points within the 18\u201326\u2009GHz frequency range, capturing the scattering parameters of various tissue types. Based on these measurements, four distinct datasets are created, incorporating features such as reflection coefficients, transmission coefficients, and frequency values. Three widely used classification algorithms\u2014\n                    <jats:italic>k<\/jats:italic>\n                    \u2010nearest neighbors (KNNs), artificial neural networks (ANNs), and Support Vector Machines (SVMs)\u2014are evaluated for their performance on these datasets. The highest classification accuracy is achieved using the KNN algorithm on the dataset containing both reflection and transmission coefficients, along with measurement frequency. Bioelectromagnetics. 00:00\u201300, 2025. \u00a9 2025 \u00a9 2025 Bioelectromagnetics Society.\n                  <\/jats:p>","DOI":"10.1002\/bem.70039","type":"journal-article","created":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T09:06:44Z","timestamp":1767690404000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Classification of Cancer Tissue With Machine Learning Algorithms Using Microwave Datasets"],"prefix":"10.1002","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3306-1163","authenticated-orcid":false,"given":"Rabia","family":"Toprak","sequence":"first","affiliation":[{"name":"Department of Electrical\u2010Electronics Engineering Karamanoglu Mehmetbey University Karaman Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2748-0660","authenticated-orcid":false,"given":"Huseyin","family":"Duysak","sequence":"additional","affiliation":[{"name":"Department of Electrical\u2010Electronics Engineering Karamanoglu Mehmetbey University Karaman Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3220-7845","authenticated-orcid":false,"given":"Zeliha Esin","family":"Celik","sequence":"additional","affiliation":[{"name":"Laboratory of Medical Pathology Selcuk University Konya Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3850-2685","authenticated-orcid":false,"given":"Giovanni Maria","family":"Sardi","sequence":"additional","affiliation":[{"name":"Institute for Microelectronics and Microsystems, National Research Council of Italy, CNR\u2010IMM Rome Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,1,6]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.3390\/s21041480"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.3390\/s21041319"},{"key":"e_1_2_10_4_1","first-page":"1014","volume-title":"Anten Teorisi: Analiz ve Tasar\u0131m","author":"Balanis C. 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