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Histopathological image (HSI) analysis enables microscopic examination of tissue samples to identify lesions and cancerous cells, but manual interpretation is time-consuming and prone to human error. A transfer learning framework is proposed to automate LCC detection using pretrained deep learning for feature extraction and classification. The approach combines an improved ShuffleNet-based feature extractor with a deep convolutional recurrent neural network, while hyperparameters are tuned via Al-Biruni Earth Radius Optimization and coati optimization, evaluated on the LC25000 database.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/al-biruni-earth-radius-optimization-with-transfer-learning-based-histopathological-image-analysis-for-lung-and-colon-cancer-detection/382594/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/al-biruni-earth-radius-optimization-with-transfer-learning-based-histopathological-image-analysis-for-lung-and-colon-cancer-detection/382594.png","ImageObject",300,407,{"name":92,"@type":93},"WPS_1786070896","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-26","2026-09-24",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is automated lung and colon cancer detection needed from histopathological images?","Question",{"text":112,"@type":113},"Manual diagnosis is time-consuming and subject to differing opinions and human error. 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Assiri 3, Rasha A. Mansouri 4,5 and Mahmoud Ragab 1,6,7  \nCitation: AlGhamdi, R.; Asar, T.O.; Assiri, F.Y.; Mansouri, R.A.; Ragab, M. Al-Biruni Earth Radius Optimization with Transfer Learning Based Histopathological Image Analysis for Lung and Colon Cancer Detection. Cancers 2023, 15, 3300. [https://](https://)[ ](https://)[doi.org/10.3390/cancers15133300](doi.org/10.3390/cancers15133300)  \nAcademic Editors: Arianna Mencattini and Helder C. R. De Oliveira  \nReceived: 1 June 2023  \nRevised: 13 June 2023  \nAccepted: 14 June 2023  \nPublished: 23 June 2023  \nCopyright: © 2023 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/)) .  \n1 Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia  \n2 Department of Biology, College of Science and Arts at Alkamil, University of Jeddah, Jeddah, Saudi Arabia  \n3 Department of Software Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia  \n4 Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia  \n5 Department of Biochemistry, Faculty of Sciences, King Abdulaziz University, Jeddah 21589, Saudi Arabia  \n6 Mathematics Department, Faculty of Science, Al-Azhar University, Naser City 11884, Cairo, Egypt  \n7 King Abdulaziz University-University of Oxford Centre for Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah 21589, Saudi Arabia  \n* Correspondence: [raalghamdi8@kau.edu.sa](raalghamdi8@kau.edu.sa)  \nSimple Summary: Histopathological image analysis can be used for the detection of lung and colon cancer by the investigation of microscopic images of tissue samples. Since manual diagnosis takes along time and is subjected to differing opinions of doctors, automated lung and colon cancer diagnosis becomes necessary. Therefore, the purpose of the study is to develop a transfer learning approach for lung and colon cancer detection on histopathological image analysis. It involves leveraging pretrained model to analyze histopathological images. In addition, the proposed model uses improved ShuffleNet with deep convolutional recurrent neural network for feature extraction and classification, respectively. Besides, Al-Biruni Earth Radius Optimization and coati optimization algorithm are employed for hyperparameter tuning process. The experimental result analysis of the proposed model on the LC25000 database shows its promising performance on lung and colon cancer diagnosis.  \nAbstract: An early diagnosis of lung and colon cancer (LCC) is critical for improved patient outcomesand effective treatment. Histopathological image (HSI) analysis has emerged as a robust tool for cancer diagnosis. HSI analysis for a LCC diagnosis includes the analysis and examination of tissue samples attained from the LCC to recognize lesions or cancerous cells. It has a significant role in the staging and diagnosis of this tumor, which aids in the prognosis and treatment planning, but a manual analysis of the image is subject to human error and is also time-consuming. Therefore, a computer-aided approach is needed for the detection of LCC using HSI. Transfer learning (TL) leverages pretrained deep learning (DL) algorithms that have been trained on a larger dataset for extracting related features from the HIS, which are then used for training a classifier for a tumor diagnosis. This manuscript offers the design of the Al-Biruni Earth Radius Optimization with Transfer Learning-base","cbCaijGDMCOUaeXL","https://ap.wps.com/l/cbCaijGDMCOUaeXL","pdf",15707826,"English","# Introduction\n# Materials and Methods\n# Results and Discussion\n# Conclusion","[{\"question\":\"Why is automated lung and colon cancer detection needed from histopathological images?\",\"answer\":\"Manual diagnosis is time-consuming and subject to differing opinions and human error. Automated analysis supports more efficient and reliable identification of lesions or cancerous cells in tissue samples.\"},{\"question\":\"What role does transfer learning play in the proposed method?\",\"answer\":\"Transfer learning leverages pretrained deep learning models to extract relevant features from histopathological images, which are then used to train a classifier for tumor diagnosis.\"},{\"question\":\"How are model hyperparameters optimized in the study?\",\"answer\":\"Hyperparameter tuning is performed using Al-Biruni Earth Radius Optimization and coati optimization, which adjust parameters for improved performance in cancer detection.\"}]","Al-Biruni Earth Radius Optimization with Transfer Learning Based Histopathological Image Analysis for Lung and Colon Cancer Detection | PDF",1790251224,48]