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Four experimental pipelines are compared, varying whether CLAHE is applied and whether data augmentation is used. Results indicate that CLAHE plus data augmentation provides the strongest performance, reporting precision 0.90, recall 0.87, F1-score 0.89, and accuracy 0.86. The study concludes that CLAHE optimization complements transfer learning especially when augmentation improves diagnostic robustness.",{"@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/enhancing-medical-image-classification-through-transfer-learning-and-clahe-optimization/468728/",{"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/enhancing-medical-image-classification-through-transfer-learning-and-clahe-optimization/468728.png","ImageObject",300,407,{"name":92,"@type":93},"Fahsai","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-03","2026-09-30",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},"What is the main goal of this study?","Question",{"text":112,"@type":113},"To assess how transfer learning combined with CLAHE optimization improves medical image classification performance, specifically for brain images.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were the experiments organized?",{"text":117,"@type":113},"Four setups were tested: normal images with/without data augmentation, and CLAHE-processed images with/without data augmentation.",{"name":119,"@type":110,"acceptedAnswer":120},"Which approach achieved the best classification performance?",{"text":121,"@type":113},"CLAHE combined with data augmentation, reaching precision 0.90, recall 0.87, F1-score 0.89, and accuracy 0.86.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},468728,1791284161,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},549768702563,"https://ap-avatar.wpscdn.com/avatar/8000c4aa63b76e948b?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786536092046926083","[1573-4056/2](1573-4056/25 Send Orders for Reprints to reprints@benthamscience.net)[5](1573-4056/25 Send Orders for Reprints to reprints@benthamscience.net)[ Send Orders for Reprints to](1573-4056/25 Send Orders for Reprints to reprints@benthamscience.net)[ ](1573-4056/25 Send Orders for Reprints to reprints@benthamscience.net)[reprints@benthamscience.net](1573-4056/25 Send Orders for Reprints to reprints@benthamscience.net)  \n1  \nCurrent Medical Imaging  \nContent list available at: [https://benthamscience.com/journals/cmir](https://benthamscience.com/journals/cmir)  \nRESEARCH ARTICLE  \nEnhancing Medical Image Classification through Transfer Learning and CLAHE Optimization  \nKamal Halloum1,* and Hamid Ez-Zahraouy1  \n1Laboratory of Condensed Matter and Interdisciplinary Sciences, CNRST Labeled Research Unit, URL-CNRST, Faculty of Sciences, Mohammed V University in Rabat, Rabat, Morocco  \nAbstract:  \nIntroduction:  \nThis paper examines the impact of transfer learning and CLAHE (Contrast Limited Adaptive Histogram Equalization) optimization on the classification of medical images, specifically brain images.  \nMethods:  \nFour different setups were tested: normal images without data augmentation, normal images with data augmentation, CLAHE-processed images without data augmentation, and CLAHE-processed images with data augmentation.  \nResults:  \nThe results show that using CLAHE combined with data augmentation significantly improves classification accuracy. Specifically, the combination of CLAHE and data augmentation achieved a precision of 0.90, a recall of 0.87, an F1-score of 0.89, and an accuracy of 0.86, outperforming the other setups.  \nConclusion:  \nThese findings highlight the effectiveness of CLAHE optimization in the context of transfer learning, particularly when data augmentation techniques are also applied, leading to an overall improvement in the performance of brain image classification models.  \nKeywords: Medical image classification, CLAHE, Transfer learning, Data augmentation, Diagnostic accuracy, Tumor.  \n\n| Article History | Received: August 03, 2024 | Revised: September 16, 2024 | Accepted: September 30, 2024 |\n| --- | --- | --- | --- |\n\n1. INTRODUCTION  \nMedical image classification plays a critical role in the diagnosis and treatment planning. Automated methods have been developed to provide accurate, objective, and fast classifications in various medical departments, particularly in pathology and dermatology, where diagnosis often relies on visual examinations of skin lesions. Recent advancements in image processing techniques, such as CLAHE, have shown promise in enhancing image quality by improving local contrast and detail visibility [1, 2] . Concurrently, Transfer  \n* Address correspondence to this author at the Laboratory of Condensed Matter and Interdisciplinary Sciences, CNRST Labeled Research Unit, URL-CNRST, Faculty of Sciences, Mohammed V University in Rabat, Morocco; [E-mail: Halloum.kamel@gmail.com](E-mail: Halloum.kamel@gmail.com)  \nLearning has emerged as a powerful approach in machine learning, allowing pretrained models to be adapted for new tasks with limited data. This study explores the combined use of CLAHE for image enhancement and Transfer Learning for feature extraction in the context of medical image classification [3] . The objective is to investigate how these techniques can synergistically improve classification accuracy, particularly in scenarios where image quality and data augmentation strategies significantly influence diagnostic outcomes [4] . This research aims to contribute insights into optimizing medical image analysis, thereby potentially enhancing diagnostic accuracy and improving patient care outcomes [5] .  \nDespite these advancements, the field still faces significant challenges. One of the primary obstacles is the scarcity of labeled medical image data, which is crucial for training robust  \nDOI: 10.2174/0115734056342623241119061744, 2025, 21, e15734056342623  \nmachi","cbCaiifJREruEzHD","https://ap.wps.com/l/cbCaiifJREruEzHD","pdf",8611951,14,"English","# Abstract\n# Introduction\n## Background and motivation\n# Methods\n## Experimental setups\n# Results\n## Performance metrics\n# Conclusion","[{\"question\":\"What is the main goal of this study?\",\"answer\":\"To assess how transfer learning combined with CLAHE optimization improves medical image classification performance, specifically for brain images.\"},{\"question\":\"How were the experiments organized?\",\"answer\":\"Four setups were tested: normal images with/without data augmentation, and CLAHE-processed images with/without data augmentation.\"},{\"question\":\"Which approach achieved the best classification performance?\",\"answer\":\"CLAHE combined with data augmentation, reaching precision 0.90, recall 0.87, F1-score 0.89, and accuracy 0.86.\"}]","Enhancing Medical Image Classification through Transfer Learning and CLAHE Optimization | PDF",1790774571,35]