{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T17:23:12Z","timestamp":1783012992061,"version":"3.54.6"},"reference-count":37,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T00:00:00Z","timestamp":1773273600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T00:00:00Z","timestamp":1773273600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["analyticalsciencejournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Microscopy Res &amp;amp; Technique"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Lung and Colon Cancer (LCC) is a prevalent type of cancer influenced by factors such as genetics, smoking, second\u2010hand smoke exposure, and alcohol consumption. Traditionally, cancer diagnosis, including grading and staging, relies on histopathology, where pathologists examine samples under a microscope. This process is time\u2010consuming and complex. To address these challenges, the Xception ResNeXT Network (XRx\u2010Net) framework is introduced for more efficient LCC detection. In this approach, histopathological images of LCC are processed through an Adaptive Bilateral Filter (ABF) for initial filtering. Next, hookNet is used to segment the cancerous cells. Features are then extracted using Wavelet texture analysis and Convolutional Neural Networks (CNNs). Later, these features are input into the LCC detection phase, where the XRx\u2010Net framework is a combination of the Xception model and ResNeXt Networks employed for classification. The framework successfully identifies various outputs, including lung benign tissue, lung adenocarcinoma, lung squamous cell carcinoma, colon benign tissue, and colon adenocarcinoma. The XRx\u2010Net framework achieves a testing accuracy of up to 91.34%, with a True Positive Rate (TPR) of 92.36% and a True Negative Rate (TNR) of 90.14%.<\/jats:p>","DOI":"10.1002\/jemt.70122","type":"journal-article","created":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T05:46:03Z","timestamp":1773380763000},"page":"1111-1129","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Detection of Lung and Colon Cancer Using\n                    <scp>XRx<\/scp>\n                    \u2010Net With Histopathological Images"],"prefix":"10.1002","volume":"89","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5829-3860","authenticated-orcid":false,"given":"Zulaikha Beevi","family":"Sulaiman","sequence":"first","affiliation":[{"name":"Department of AI&amp;DS Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College  Chennai Tamil Nadu India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sarojini","family":"Balakrishnan","sequence":"additional","affiliation":[{"name":"Department of Computer Science Avinashilingam Institute for Home Science and Higher Education For Women  Coimbatore Tamil Nadu India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rama","family":"Gaikwad","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering Anantrao Pawar College of Engineering and Research  Pune India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8119-8170","authenticated-orcid":false,"given":"Sreenu","family":"Ponnada","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering Vishnu Institute of Technology  Bhimavaram Andhra Pradesh India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,3,12]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3189374"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/ima.22569"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.3390\/cancers15133300"},{"key":"e_1_2_9_5_1","first-page":"258","article-title":"Enhanced Feature Selection for Imbalanced Microarray Cancer Gene Classification Using Chaotic Salp Swarm Algorithm","author":"Aliyu B. 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