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This research proposes a lightweight, parameter-efficient 1D CNN enhanced with squeeze-and-excitation layers for mobile-embedded use, classifying lung and colon cancers from digital pathology images. Extensive experiments on the LC25000 datasets report 100% accuracy, showing state-of-the-art performance.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/healthcare/","Healthcare",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/lung-and-colon-cancer-detection-using-a-deep-ai-model/331556/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/lung-and-colon-cancer-detection-using-a-deep-ai-model/331556.png","ImageObject",300,407,{"name":42,"@type":43},"Seraphina","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-22","2026-09-21",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",6,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"Why is early detection of lung and colon cancer important?","Question",{"text":63,"@type":64},"Early and accurate detection improves treatment effectiveness and patient outcomes. 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Lung and Colon Cancer Detection Using a Deep AI Model. Cancers 2024, 16, 3879. [https://](https://)[ ](https://)[doi.org/10.3390/cancers16223879](doi.org/10.3390/cancers16223879)  \nAcademic Editor: Hiroyuki Yoshida  \nReceived: 11 September 2024  \nRevised: 31 October 2024  \nAccepted: 10 November 2024  \nPublished: 20 November 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \nDepartment of Computer and Data Sciences, Truman State University, Kirksville, MO 63501, USA;  \n[ritikatheeng2002@gmail.com](ritikatheeng2002@gmail.com) (R.L.); [nguyenthucanh.35@gmail.com](nguyenthucanh.35@gmail.com) (A.N.)  \n* [Correspondence: nazmul.ruet@gmail.com](Correspondence: nazmul.ruet@gmail.com)  \n† This paper is an extended version of our paper published in Shahadat, N. Lung and Colon Cancer Histopathological Image Classification Using 1D Convolutional Channel-based Attention Networks. In Proceedings of the International FLAIRS Conference Proceedings, Sandestin Beach, FL, USA, 19–21 May 2024 .  \n‡ These authors contributed equally to this work.  \nSimple Summary: Lung and colon cancer are among the leading causes of cancer-related deaths globally, emphasizing the critical need for early and precise detection to improve treatment and patient outcomes. This research introduces a novel deep learning model for efficient lung and colon cancer detection, aiming to address the limitations of existing computationally intensive models. This study proposes a lightweight, parameter-efficient model suitable for mobile devices, utilizing a 1D convolutional neural network enhanced with Squeeze-and-Excitation layers. By achieving 100% accuracy on a large dataset of histopathological images, this research provides a significant advancement in cancer detection technology. The findings hold the potential to revolutionize medical diagnostics by enabling faster, more accessible, and reliable cancer screening, potentially leading to earlier interventions and improved survival rates.  \nAbstract: Lung and colon cancers are among the leading causes of cancer-related mortality worldwide. Early and accurate detection of these cancers is crucial for effective treatment and improved patient outcomes. False or incorrect detection is harmful. Accurately detecting cancer in a patient’stissue is crucial to their effective treatment. While analyzing tissue samples is complicated and time-consuming, deep learning techniques have made it possible to complete this process more efficiently and accurately. As a result, researchers can study more patients in a shorter amount of time and at a lower cost. Much research has been conducted to investigate deep learning models that require great computational ability and resources. However, none of these have had a 100% accurate detection rate for these life-threatening malignancies. Misclassified or falsely detecting cancer can have very harmful consequences. This research proposes a new lightweight, parameter-efficient, and mobile-embedded deep learning model based on a 1D convolutional neural network with squeeze-and-excitation layers for efficient lung and colon cancer detection. This proposed model diagnoses and classifies lung squamous cell carcinomas and adenocarcinoma of the lung and colon from digital pathology images. Extensive experiment demonstrates that our proposed model achieves 100% accuracy for detecting lung, colon, and lung and colon cancers from the histopathological (LC25000) lung and colon datasets, which is considered the best accuracy ","cbCaij9SGYVjZxMn","https://ap.wps.com/l/cbCaij9SGYVjZxMn","pdf",1180088,28,"English","# Introduction\n## Related concepts and motivation\n## Cancer background and detection challenges","[{\"question\":\"Why is early detection of lung and colon cancer important?\",\"answer\":\"Early and accurate detection improves treatment effectiveness and patient outcomes. Incorrect detection can be harmful by misguiding care.\"},{\"question\":\"What model does the research propose for cancer detection?\",\"answer\":\"The study proposes a lightweight, parameter-efficient deep learning model based on a 1D convolutional neural network with squeeze-and-excitation layers.\"},{\"question\":\"How is the proposed method evaluated and what result is reported?\",\"answer\":\"Experiments use histopathological image datasets including LC25000, and the model reports 100% accuracy for detecting lung, colon, and combined cases.\"}]","Lung and Colon Cancer Detection Using a Deep AI Model | PDF",1789993976,71]