Computers in biology and medicine · 2025
A deep architecture based on attention mechanisms for effective end-to-end detection of early and mature malaria parasites in a realistic scenario.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Zedda Luca, Loddo Andrea, Di Ruberto Cecilia
Аннотация
BACKGROUND: Malaria is a critical and potentially fatal disease caused by the Plasmodium parasite and is responsible for more than 600,000 deaths globally. Early and accurate detection of malaria parasites is crucial for effective treatment, yet conventional microscopy faces limitations in variability and efficiency.
METHODS: We propose a novel computer-aided detection framework based on deep learning and attention mechanisms, extending the YOLO-SPAM and YOLO-PAM models. Our approach facilitates the detection and classification of malaria parasites across all infection stages and supports multi-species identification.
RESULTS: The framework was evaluated on three publicly available datasets, demonstrating high accuracy in detecting four distinct malaria species and their life stages. Comparative analysis against state-of-the-art methodologies indicates significant improvements in both detection rates and diagnostic utility.
CONCLUSION: This study presents a robust solution for automated malaria detection, offering valuable support for pathologists and enhancing diagnostic practices in real-world scenarios.
METHODS: We propose a novel computer-aided detection framework based on deep learning and attention mechanisms, extending the YOLO-SPAM and YOLO-PAM models. Our approach facilitates the detection and classification of malaria parasites across all infection stages and supports multi-species identification.
RESULTS: The framework was evaluated on three publicly available datasets, demonstrating high accuracy in detecting four distinct malaria species and their life stages. Comparative analysis against state-of-the-art methodologies indicates significant improvements in both detection rates and diagnostic utility.
CONCLUSION: This study presents a robust solution for automated malaria detection, offering valuable support for pathologists and enhancing diagnostic practices in real-world scenarios.