[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127466-en":3,"doc-seo-127466-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127466,962084925290,"Ophelia","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",7,"Healthcare","Automated Web-Based Malaria Detection System with Machine Learning and Deep Learning Techniques","Malaria parasites create a major global health burden, demanding accurate infection detection to support timely treatment and effective control. Existing automated approaches often struggle with sufficient accuracy and broad generalizability, motivating more comprehensive learning-based solutions. This work formulates a deep learning method for classifying malaria-infected cells using CNNs and transfer learning models including VGG19, InceptionV3, and Xception, trained on NIH datasets and evaluated using accuracy, precision, recall, and F1-score. Deep CNNs achieve the best results (about 97% accuracy), with Xception and InceptionV3 following closely, while SVM reaches lower accuracy (about 83%). A web interface enables users to upload blood smear images for malaria detection.","Communications in Computer and Information Science (CCIS), Volume: PanAfriCon: Pan African Conference on Artificial Intelligence, Issue: AI for Health  \nAutomated Web-Based Malaria Detection System with Machine Learning and Deep Learning Techniques  \nAbraham G Taye1,2 · Eshetu Negash1 · Moges Abebe1 · Sador Yonas1 · Yared Minwyelet1· Melkamu Hunegnaw Asmare1,3  \n1 Center of Biomedical Engineering, Addis Ababa Institute of Technology, Addis Ababa University, King George VI St Addis Ababa 1000 Addis Ababa, Ethiopia.  \n2Opus College of Engineering, Marquette University, 1250 W Wisconsin Ave, Milwaukee, WI, 53233  \n3Leuven Center for Affordable Healthcare Technology, KU Leuven| Campus Groep T, Andreas Vesaliusstraat 13, 3000 Leuven, BELGIUM  \nReceived: / Accepted:  \nAbstract: Malaria parasites pose a significant global health burden, causing widespread suffering and mortality. Detecting malaria infection accurately is crucial for effective treatment and control. However, existing automated detection techniques have shown limitations in terms of accuracy and generalizability. Many studies have focused on specific features without exploring more comprehensive approaches. In our case, we formulate a deep learning technique for malariainfected cell classification using traditional CNNs and transfer learning models notably VGG19, InceptionV3, and Xception. The models were trained using NIH datasets and tested using different performance metrics such as accuracy, precision, re-call, and F1-score. The test results showed that deep CNNs achieved the highest accuracy-97%, followed by Xception with an accuracy of 95% . A machine learning model SVM achieved an accuracy of 83%, while an Inception-V3 achieved an accuracy of 94% . Furthermore, the system can be accessed through a web interface, where users can upload blood smear images for malaria detection.  \nKey Words: Malaria, Automated Detection, Machine Learning, Deep Convolutional Neural Networks, Transfer Learning  \n1. Introduction  \nBackground  \nMalaria is a life-threatening infectious disease, that has a significant impact on global health. As of more recent data, it is estimated that around half of the world's population is at risk of contracting malaria. In 2019, there were approximately 229 million cases of malaria reported worldwide. This resulted in an estimated 409,000 deaths, affecting vulnerable populations such as pregnant women and infants and small children five years old. Malaria continues to pose a substantial burden on public health, emphasizing the urgent need for effective prevention, diagnosis, and treatment strategies [1] .  \nMalaria is a deadly human disease caused by organisms called Plasmodium genus. Malaria infections are largely spread among humans via the bites of so-called malaria vectors — adult females belonging to Anopheles-type mosquitos. Widely speaking, Plasmodium infection is caused by many species of Plasmodium genus protozoa [2] . Figure one shows the lifecycle of the Plasmodium parasite. All five species’ life and infective cycles are similar. And their morphology and body shapes look alike on animals [3] .  \nFigure 1: The life cycle of malaria parasite [5  \nAccording to the World Health Organization (WHO) statistics, malaria infection causes over one million human infections each year, with a particularly alarming impact in certain regions. For instance, as referenced in [5], there were approximately 219 million reported cases of malaria across 87 countries affected by the epidemic. Africa is the most affected continent with 95% of all malaria cases reported and 96% of all deaths [4,5]. The WHO report also highlights the devastating toll of malaria in Africa, where it accounts for a significant proportion of the top ten causes of mortality.  \nMalaria is among the most prominent reasons for being ill or dying. Malaria infections have been recorded in more than 75 percent of the landscapes below 2000m (about 1.24 mi) elevated levels in Ethiopia [7] . In Ethiopia, the ","cbCaihqZc8i5ZSE3","https://ap.wps.com/l/cbCaihqZc8i5ZSE3","pdf",2360649,2,1,38,"English","en",105,"# Abstract\n# Introduction\n## Malaria burden and epidemiology\n## Importance of early detection and diagnosis\n## Image analysis and deep learning for computer-aided diagnosis","[{\"question\":\"What problem does the automated system target?\",\"answer\":\"It targets accurate malaria detection by classifying malaria-infected cells from blood smear images, addressing limitations of earlier automated techniques in accuracy and generalizability.\"},{\"question\":\"Which deep learning and transfer learning models are evaluated?\",\"answer\":\"The study evaluates CNN-based approaches and transfer learning models including VGG19, InceptionV3, and Xception, trained on NIH datasets.\"},{\"question\":\"How can the system be used in practice?\",\"answer\":\"The system is provided through a web interface where users upload blood smear images to perform malaria detection.\"}]","Automated Web-Based Malaria Detection System with Machine Learning and Deep Learning Techniques | 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problem does the automated system target?","Question",{"text":76,"@type":77},"It targets accurate malaria detection by classifying malaria-infected cells from blood smear images, addressing limitations of earlier automated techniques in accuracy and generalizability.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which deep learning and transfer learning models are evaluated?",{"text":81,"@type":77},"The study evaluates CNN-based approaches and transfer learning models including VGG19, InceptionV3, and Xception, trained on NIH datasets.",{"name":83,"@type":74,"acceptedAnswer":84},"How can the system be used in practice?",{"text":85,"@type":77},"The system is provided through a web interface where users upload blood smear images to perform malaria 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