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Evaluation on 3000 MR images using a free dataset yields accuracy between 97.83% and 99.67% across four models.",{"@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/explainable-convolutional-neural-networks-for-brain-cancer-detection-and-localisation/379219/",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/explainable-convolutional-neural-networks-for-brain-cancer-detection-and-localisation/379219.png","ImageObject",300,407,{"name":42,"@type":43},"Rizky","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-29","2026-09-24",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",5,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What is the main goal of the proposed method?","Question",{"text":63,"@type":64},"To detect and localize brain cancer using analysis of magnetic resonance images.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How does the method provide explainability?",{"text":68,"@type":64},"By using convolutional neural networks together with class activation mapping to highlight image areas related to brain cancer from the model’s perspective.",{"name":70,"@type":61,"acceptedAnswer":71},"Which models are evaluated and what performance is reported?",{"text":72,"@type":64},"The method is evaluated using four models—VGG16, ResNet50, Alex_Net, and MobileNet—with accuracy reported from 97.83% to 99.67% for brain cancer detection.","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},379219,1791401487,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,100,105,109,114,119,122,126],{"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":55,"doc_module":4,"doc_module_name":25,"category_name":97,"show_sort_weight":98,"slug":99},"Comic",60,"comic",{"id":101,"doc_module":4,"doc_module_name":25,"category_name":102,"show_sort_weight":103,"slug":104},6,"Technology",50,"technology",{"id":106,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":107,"slug":108},7,40,"healthcare",{"id":110,"doc_module":4,"doc_module_name":25,"category_name":111,"show_sort_weight":112,"slug":113},8,"Research & Report",30,"research-report",{"id":115,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":55,"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":106,"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":107},962085564807,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","sensors   \nArticle  \nExplainable Convolutional Neural Networks for Brain Cancer Detection and Localisation  \nFrancesco Mercaldo 1,2, *, Luca Brunese 1, Fabio Martinelli 2, Antonella Santone 1 and Mario Cesarelli 3  \nCitation: Mercaldo, F.; Brunese, L.; Martinelli, F.; Santone, A.; Cesarelli, M. Explainable Convolutional Neural Networks for Brain Cancer Detection and Localisation. Sensors 2023, 23, 7614. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)s23177614  \nAcademic Editor: Guang-Jer Wu  \nReceived: 10 July 2023  \nRevised: 7 August 2023  \nAccepted: 31 August 2023  \nPublished: 2 September 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 Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise,  \n86100 Campobasso, Italy; [luca.brunese@unimol.it](luca.brunese@unimol.it) (L.B.); [antonella.santone@unimol.it](antonella.santone@unimol.it) (A.S.)  \n2 Institute for Informatics and Telematics, National Research Council of Italy, 56121 Pisa, Italy  \n3 Department of Engineering, University of Sannio, 82100 Benevento, Italy; mcesarelli@unisannio.it  \n* Correspondence: francesco.mercaldo@unimol.it or francesco.mercaldo@iit.cnr.it  \nAbstract: Brain cancer is widely recognised as one of the most aggressive types of tumors. In fact, approximately 70% of patients diagnosed with this malignant cancer do not survive. In this paper, we propose a method aimed to detect and localise brain cancer, starting from the analysis of magnetic resonance images. The proposed method exploits deep learning, in particular convolutional neural networks and class activation mapping, in order to provide explainability by highlighting the areas of the medical image related to brain cancer (from the model point of view) . We evaluate the proposed method with 3000 magnetic resonances using a free available dataset. The results we obtained are encouraging. We reach an accuracy ranging from 97.83% to 99.67% in brain cancer detection by exploiting four different models: VGG16, ResNet50, Alex_Net, and MobileNet, thus showing the effectiveness of the proposed method.  \nKeywords: brain; machine learning; deep learning; explainability  \n1. Introduction  \nBrain cancer is characterised by the growth of abnormal cells or a cluster of cells in the brain or its surrounding structures. Brain tumors, a type of brain cancer, can be categorised as either malignant (cancerous) or benign (non-cancerous) . Malignant brain tumors have the ability to invade nearby tissues and metastasise to other parts of the body, whereas benign tumors typically do not invade nearby tissues or spread. Brain tumors area significant cause of cancer-related fatalities in children under the age of 20 . In fact, brain tumors have surpassed acute lymphoblastic leukemia as the primary cause of solid cancer deaths in this particular age group. This emphasises the importance of understanding and addressing brain tumors in pediatric oncology ([http://blog.braintumor.org/](http://blog.braintumor.org/), accessed on 20 August 2023) . Brain tumors are, indeed, a significant cause of solid cancer deaths among young adults between the ages of 20 and 39 . They rank as the third leading cause of solid cancer-related deaths in this age group. Each year, more than 5000 individuals lose their lives to brain tumors. Furthermore, in the United Kingdom, it is estimated that at least 102,000 children and adults are currently living with a brain tumor. This statistic underscores the prevalence and impact of brain tumors on individuals and their families ([https://www.cancerresearchuk.org/health-professional/cancer-statistics/statistics](https://www.cance","cbCaitNVWv0YzdwF","https://ap.wps.com/l/cbCaitNVWv0YzdwF","pdf",741837,16,"English","# Abstract\n# Keywords\n# 1. Introduction","[{\"question\":\"What is the main goal of the proposed method?\",\"answer\":\"To detect and localize brain cancer using analysis of magnetic resonance images.\"},{\"question\":\"How does the method provide explainability?\",\"answer\":\"By using convolutional neural networks together with class activation mapping to highlight image areas related to brain cancer from the model’s perspective.\"},{\"question\":\"Which models are evaluated and what performance is reported?\",\"answer\":\"The method is evaluated using four models—VGG16, ResNet50, Alex_Net, and MobileNet—with accuracy reported from 97.83% to 99.67% for brain cancer detection.\"}]","Explainable Convolutional Neural Networks for Brain Cancer Detection and Localisation | PDF",1790234643]