[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124456-en":3,"doc-seo-124456-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},124456,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Traditional machine learning in biomedical image analysis - before you go too deep - mini review","Traditional machine learning (TML) algorithms remain essential for biomedical image analysis, providing advantages in multimodal data integration, interpretability, computational efficiency, and robustness on smaller datasets. This mini-review surveys TML applications across major biomedical imaging modalities and summarizes core concepts and implementation practices, contrasting TML’s strengths with deep learning’s dominance. It emphasizes TML’s value in multimodal processing, limited data, interpretability-driven needs, and rapid prototyping, supported by democratized tooling and clinically validated outcomes.","TYPE Mini Review  \nPUBLISHED 29 January 2026 DOI 10.3389/frai.2026.1695230  \nOPEN ACCESS  \nEDITED BY  \nAzhar Imran,  \nAir University, Pakistan  \nREVIEWED BY  \nBaidaa Mutasher, Thiqar University, Iraq Sachin Harne,  \nRaisoni Group of Institutions, India  \n*CORRESPONDENCE  \nElizaveta Chechekhina  \n [voynovaes.pharm@gmail.com](voynovaes.pharm@gmail.com)  \nRECEIVED 29 August 2025  \nREVISED 01 January 2026  \nACCEPTED 16 January 2026  \nPUBLISHED 29 January 2026  \nCITATION  \nChechekhina E, Voloshin N, Solopov M, Tyurin-Kuzmin P and Kulebyakin K (2026) Traditional machine learning in biomedical image analysis: before you go too deep.  \nFront. Artif. Intell. 9:1695230 .  \ndoi: 10.3389/frai.2026.1695230  \nCOPYRIGHT  \n© 2026 Chechekhina, Voloshin, Solopov, Tyurin-Kuzmin and Kulebyakin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nTraditional machine learning in biomedical image analysis: before you go too deep  \nElizaveta Chechekhina 1*, Nikita Voloshin 1, Maksim Solopov 2, Pyotr Tyurin-Kuzmin 1 and Konstantin Kulebyakin 1  \n1Medical Research and Educational Institute, Lomonosov Moscow State University, Moscow, Russia, 2V. K. Gusak Institute of Emergency and Reconstructive Surgery, Donetsk, Russia  \nTraditional machine learning (TML) algorithms remain indispensable tools for the analysis of biomedical images, offering significant advantages in multimodal data integration, interpretability, computational efficiency, and robustness on smaller datasets. This review provides a comprehensive examination of TML applications across a broad spectrum of biomedical imaging modalities, highlighting its core principles, practical implementation, and unique benefits in the era of deep learning (DL) . We outline the fundamental concepts of machine learning and describe key biomedical imaging tasks successfully addressed by TML. We also highlight the most popular platforms, which empower clinicians and researchers to utilize TML. DL now dominates many areas of medical image analysis due to superior performance and end-to-end feature learning. Using the most prominent examples, we analyze how TML retains unique value for applications with multimodal data processing, limited data, interpretability requirements, or rapid prototyping needs. Supported by increasingly democratized tools and validated by robust clinical studies, TML remains a vital methodology for extracting quantitative and qualitative insights from biomedical image data, ensuring its continued relevance in both research and clinical practice.  \nKEYWORDS  \nbiomedical image analysis, object classification, radiomics, semantic segmentation, traditional machine learning  \nIntroduction  \nBy 2025, deep learning (DL) has achieved remarkable progress in biomedical imaging, with vision large language models (vLLMs) now setting new standards for automated interpretation and analysis (Li et al., 2023; Lan et al., 2025). Yet, despite the complexity and high competence of these modern approaches, much earlier and simpler traditional machine learning (TML) methods remain not only in use but actively thrive. For example, according to Dimensions citation data available via Altmetric, the ImageJ WEKA trainable segmentation paper has accumulated more than 2,000 citations overall, with more than 800 of them appearing in just the last 2 years, reflecting sustained growth of ImageJ WEKA usage in recent biomedical and microscopic imaging studies. Importantly, the fieldclassification of these citing articles is dominated by “Biomedical and Clinical Sciences”and “Biological Sciences,” indicating that Trainable Weka","cbCaikiyQbmSVeSw","https://ap.wps.com/l/cbCaikiyQbmSVeSw","pdf",1525288,1,"English","en",105,"# Introduction\n## Why traditional machine learning remains relevant\n## Purpose and scope of the mini-review","[{\"question\":\"Why does traditional machine learning remain widely used in biomedical image analysis despite deep learning advances?\",\"answer\":\"TML continues to attract adoption due to advantages such as interpretability, robustness on smaller datasets, computational efficiency, and practical alignment with biological and clinical workflows.\"},{\"question\":\"What conditions are highlighted where TML can be the most suitable choice?\",\"answer\":\"The mini-review reviews scenarios including limited dataset size, hardware constraints, and the need for biological interpretability, outlining “middle-ground” biomedical data processing cases.\"},{\"question\":\"Which biomedical imaging tasks and tool areas does the review focus on?\",\"answer\":\"The review covers core TML principles and major biomedical imaging tasks, highlighting applications such as object classification and radiomics workflows, including semantic segmentation approaches.\"}]","Traditional machine learning in biomedical image analysis - before you go too deep - mini review | PDF",1785822409,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"traditional-machine-learning-in-biomedical-image-analysis-before-you-go-too-deep-mini-review","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/traditional-machine-learning-in-biomedical-image-analysis-before-you-go-too-deep-mini-review/124456/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why does traditional machine learning remain widely used in biomedical image analysis despite deep learning advances?","Question",{"text":74,"@type":75},"TML continues to attract adoption due to advantages such as interpretability, robustness on smaller datasets, computational efficiency, and practical alignment with biological and clinical workflows.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What conditions are highlighted where TML can be the most suitable choice?",{"text":79,"@type":75},"The mini-review reviews scenarios including limited dataset size, hardware constraints, and the need for biological interpretability, outlining “middle-ground” biomedical data processing cases.",{"name":81,"@type":72,"acceptedAnswer":82},"Which biomedical imaging tasks and tool areas does the review focus on?",{"text":83,"@type":75},"The review covers core TML principles and major biomedical imaging tasks, highlighting applications such as object classification and radiomics workflows, including semantic segmentation approaches.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":28,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":28,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]