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Rada, Lavdie, Azzawi, Inass, Kumar, Preet, Brito-Loeza, Carlos Francisco, Karabağ, Cefa and Reyes-Aldasoro, Constantino C. (2024) Image processing and machine learning techniques for Chagas disease detection and identification. In: 28th UK Conference on Medical Image Understanding and Analysis - MIUA, 24-26 July 2024, Manchester Metropolitan University.
Chagas disease, caused by the Trypanosoma cruzi parasite, poses a significant health threat, particularly in Latin America, with millions affected globally. This research introduces a novel approach using deep learning techniques for the automated detection of Trypanosoma cruzi in blood smear images provided by Zoonoses Laboratory (CIR) in Mexico. Advanced deep learning architectures like Faster RCNN, RetinaNet, YOLOv8, and FCOS have been adapted, trained, and compared with each other in terms of the detection accuracy of each image. Our selection of those models is based on their ability to swiftly and accurately detect anomalies, measured through rigorous assessment using pivotal metrics like Mean Average Precision (mAP) across varying Intersection over Union (IoU) thresholds. Notably, the YOLOv8 model has showcased outstanding performance, boasting a remarkable mAP score of 0.951 for parasite detection and localisation. Specifically, YOLOv8 outperforms with a leading mAP of 0.951 at 50% IoU and maintains commendable precision with a score of 0.594 for IoU thresholds ranging from 50% to 95%. This research reduces dependence on skilled manual analysis holding a significant implications for healthcare in Chagas-affected regions by providing a rapid, automated solution to disease detection. This work has the potential to revolutionise diagnostics in resource-limited settings. Moreover, the models’ adaptability to other parasitic infections enhances their global health impact.
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