{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-deep-architecture-based-on-attention","title":"A deep architecture based on attention mechanisms for effective end-to-end detection of early and mature malaria parasites in a realistic scenario","arxiv_id":null,"date":"2025-01-26","proceeding":"Computers in Biology and Medicine 2025 1","authors":["Luca Zedda","Andrea Loddo","Cecilia Di Ruberto"],"abstract":"Background: Malaria is a critical and potentially fatal disease caused by the Plasmodium parasite and is\r\nresponsible for more than 600,000 deaths globally. Early and accurate detection of malaria parasites is crucial\r\nfor effective treatment, yet conventional microscopy faces limitations in variability and efficiency.\r\nMethods: We propose a novel computer-aided detection framework based on deep learning and attention\r\nmechanisms, extending the YOLO-SPAM and YOLO-PAM models. Our approach facilitates the detection and\r\nclassification of malaria parasites across all infection stages and supports multi-species identification.\r\nResults: The framework was evaluated on three publicly available datasets, demonstrating high accuracy\r\nin detecting four distinct malaria species and their life stages. Comparative analysis against state-of-the-art\r\nmethodologies indicates significant improvements in both detection rates and diagnostic utility.\r\nConclusion: This study presents a robust solution for automated malaria detection, offering valuable support\r\nfor pathologists and enhancing diagnostic practices in real-world scenarios.","url_abs":"https://www.sciencedirect.com/science/article/pii/S001048252500054X?dgcid=coauthor","url_pdf":"https://www.sciencedirect.com/science/article/pii/S001048252500054X/pdfft?md5=ae60b11f19337949661872f014052f88&pid=1-s2.0-S001048252500054X-main.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-deep-architecture-based-on-attention","repo_url":"https://github.com/Snarci/YOLO-Para","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"diagnostic","task_name":"Diagnostic"},{"task_slug":"malaria-falciparum-detection","task_name":"Malaria Falciparum Detection"},{"task_slug":"malaria-malariae-detection","task_name":"Malaria Malariae Detection"},{"task_slug":"malaria-ovale-detection","task_name":"Malaria Ovale Detection"},{"task_slug":"malaria-vivax-detection","task_name":"Malaria Vivax Detection"},{"task_slug":"medical-diagnosis","task_name":"Medical Diagnosis"},{"task_slug":"medical-image-analysis","task_name":"Medical Image Analysis"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"medical-image-detection","task_name":"medical image detection"}],"methods":[{"method_slug":"yolov8","method_name":"YOLOv8"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/malaria-falciparum-detection-on-m5-malaria","task":"Malaria Falciparum Detection","dataset":"M5-Malaria Dataset","model":"YOLO Para","rank_in_archive_order":1,"of":1,"metrics":{"AP":"71.0"},"uses_additional_data":false},{"leaderboard":"/sota/malaria-falciparum-detection-on-mp-idb","task":"Malaria Falciparum Detection","dataset":"MP-IDB","model":"YOLO Para","rank_in_archive_order":1,"of":1,"metrics":{"AP":"86.5"},"uses_additional_data":false},{"leaderboard":"/sota/malaria-malariae-detection-on-mp-idb","task":"Malaria Malariae Detection","dataset":"MP-IDB","model":"YOLO Para","rank_in_archive_order":1,"of":1,"metrics":{"AP":"94.9"},"uses_additional_data":false},{"leaderboard":"/sota/malaria-ovale-detection-on-mp-idb","task":"Malaria Ovale Detection","dataset":"MP-IDB","model":"YOLO Para","rank_in_archive_order":1,"of":1,"metrics":{"AP":"95.1"},"uses_additional_data":false},{"leaderboard":"/sota/malaria-vivax-detection-on-mp-idb","task":"Malaria Vivax Detection","dataset":"MP-IDB","model":"YOLO Para","rank_in_archive_order":1,"of":1,"metrics":{"AP":"88.3"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}