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Five ML models (SVM, Naïve Bayes, decision tree, KNN, neural network) were evaluated with 5-, 7-, and 10-fold cross-validation, using wavelet, GLCM, and histogram features with statistical selection of 114 key attributes. KNN with sevenfold cross-validation reached 98% accuracy, while a CNN achieved 98.23% for automated classification.",{"@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/machine-learning-based-multi-class-classification-and-grading-of-squamous-cell-carcinoma-in-optical-microscopy-research-article/373784/",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/machine-learning-based-multi-class-classification-and-grading-of-squamous-cell-carcinoma-in-optical-microscopy-research-article/373784.png","ImageObject",300,407,{"name":42,"@type":43},"Well Done","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-24","2026-09-23",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",10,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"Why is tissue grading important in SCC diagnosis?","Question",{"text":63,"@type":64},"Tissue grading is crucial for disease diagnosis and treatment, and it directly supports accurate clinical decision-making.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"Which models were trained and how were they evaluated?",{"text":68,"@type":64},"Five ML models (SVM, Naïve Bayes, decision tree, KNN, neural network) were trained and tested using 5-, 7-, and 10-fold cross-validation.",{"name":70,"@type":61,"acceptedAnswer":71},"What accuracy was achieved for automated SCC classification?",{"text":72,"@type":64},"KNN with sevenfold cross-validation achieved 98% accuracy, and the CNN model achieved 98.23% accuracy for automated classification.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},373784,1790293592,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,110,115,120,123,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":108,"slug":109},7,40,"healthcare",{"id":111,"doc_module":4,"doc_module_name":25,"category_name":112,"show_sort_weight":113,"slug":114},8,"Research & Report",30,"research-report",{"id":116,"doc_module":4,"doc_module_name":25,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":25,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":55,"slug":125},"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":97,"slug":129},19,"General","general",{"code":4,"msg":82,"data":131},{"doc_id":79,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":107,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":139,"language":140,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":12,"update_tm":144,"read_time":145},5909892210082,"https://ap-avatar.wpscdn.com/avatar/56001069da27c9f41e8?x-image-process=image/resize,m_fixed,w_180,h_180&k=1789614185077863061","Microscopy Research and Technique  \nRESEARCH ARTICLE  OPEN ACCESS   \nMachine Learning Based Multi-Class Classification and Grading ofSquamous Cell Carcinoma in Optical Microscopy  \nSindhoora Kaniyala Melanthota1 | Spandana K. U.1 | Raghavendra U.2 | Sharada Rai3 | Rakshatha Nayak3 | Yuri V. Kistenev4 | Suranjan Shil5 | K. K. Mahato1 | Nirmal Mazumder1  \n1Department of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India | 2Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka,  \nIndia | 3Department of Pathology, Kasturba Medical College, Manipal academy of Higher Education, Mangalore, Karnataka, India | 4Department of General and Experimental Physics, Tomsk State University, Tomsk, Russia | 5Manipal Centre for Natural Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India  \nCorrespondence: Nirmal Mazumder ([nirmal.mazumder@manipal.edu](nirmal.mazumder@manipal.edu))  \nReceived: 16 October 2024 | Revised: 12 February 2025 | Accepted: 11 June 2025  \nReview Editor: Paolo Bianchini  \nFunding: This work was supported by the Department of Science and Technology (DST)—Sciences and Engineering Research board (SERB), Government of India, India (ECR/2016/001944), Sciences and Engineering Research board (SERB), Government of India, India (MTR/2020/000058), Global Innovation and Technology Alliance (GITA), Department of Science and Technology (DST), Government of India, India (GITA/DST/TWN/P-95/2021), and Indian Council of Medical Research (ICMR), Government of India, India (ITR/Ad-hoc/43/2020-21) Government of India, India.  \nKeywords: convolutional neural network | histopathology | machine learning | optical microscopy | squamous cell carcinoma | tissue grading  \nABSTRACT  \nHistopathological tissue grading is critical for disease diagnosis and treatment, but manual grading is labor-intensive and timeconsuming, requiring expert pathologists. This study presents an efficient analysis of squamous cell carcinoma (SCC) histopathological images using machine learning (ML) and deep learning (DL) models. Five ML models—support vector machine, Naïve Bayes, decision tree, k-nearest neighbor (KNN), and neural network—were trained with 5-, 7-, and 10-fold cross-validation. Discrete wavelet transform along with gray level co-occurrence matrix and histogram features extracted 360 features per image, and Student's t-test selected 114 key features. Among ML models, KNN with sevenfold cross-validation achieved 98% accuracy. Additionally, a convolutional neural network (CNN) trained achieved 98.23% accuracy in automated classification. These results suggest that combining ML for feature analysis with interpretable DL models can lead to more accurate and efficient SCC grading, reducing reliance on manual pathology.  \n1 | Introduction  \nOver 90% of oral cancers are squamous cell carcinoma (SCC), a varied group of cancers originating from the mucosal lining of the mouth (Du et al. 2020; Warnakulasuriya and Greenspan 2020). It is a subtype of head and neck SCC, which ranks seventh among all cancers globally (Perdomo et al. 2016). The World Health Organization estimates that there are 657,000 new cases and more  \nthan 330,000 fatalities worldwide each year. It has been shown that the incidence rates of SCC are noticeably higher in South Asian nations. India has most of them, accounting for one-third worldwide (Anwar et al. 2020). The risk factors include the use of tobacco and alcohol, bad dental hygiene, contact with the human papillomavirus, genetic makeup, way of life, ethnicity, and region (Anwar et al. 2020). Early diagnosis of SCC is essential for efficient therapy, increased survival odds, and decreased morbidity and mortality  \nThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any mediu","cbCaikYo9oPJUV8G","https://ap.wps.com/l/cbCaikYo9oPJUV8G","pdf",4344597,13,"English","# Introduction\n# Summary","[{\"question\":\"Why is tissue grading important in SCC diagnosis?\",\"answer\":\"Tissue grading is crucial for disease diagnosis and treatment, and it directly supports accurate clinical decision-making.\"},{\"question\":\"Which models were trained and how were they evaluated?\",\"answer\":\"Five ML models (SVM, Naïve Bayes, decision tree, KNN, neural network) were trained and tested using 5-, 7-, and 10-fold cross-validation.\"},{\"question\":\"What accuracy was achieved for automated SCC classification?\",\"answer\":\"KNN with sevenfold cross-validation achieved 98% accuracy, and the CNN model achieved 98.23% accuracy for automated classification.\"}]","Machine Learning Based Multi-Class Classification and Grading of Squamous Cell Carcinoma in Optical Microscopy - Research article | PDF",1790200447,33]