ParasiteNet: Deep Learning-Based Identification of Parasitic Helminth Eggs in Microscopic Images.

Feasibility of a convolutional neural network (CNN)-based approach for the detection of Leishmania parasites in microscopic fields as an adjunct diagnostic tool for leishmaniasis

Leishmaniasis, caused by protozoan parasites of the genus Leishmania, is ranked among the top 10 neglected tropical diseases (NTDs) by the World Health Organization and constitutes a significant global public health burden. In Sri Lanka, cutaneous leishmaniasis is the predominant clinical form, and the disease is consi...

A Resnet50-Based Machine Learning Diagnostic Model for Malaria Detection and Diagnosis

Malaria remains a major global health challenge, particularly in sub-Saharan Africa, where it accounts for the majority of morbidity and mortality cases. Conventional diagnostic methods, such as microscopic examination of Giemsa-stained blood smears, though considered the gold standard, are time-consuming and prone...

Automated Detection of Malaria Parasites Using Image Processing and Convolutional Neural Network Classifier.

Malaria, being one of the most dangerous parasite diseases endangering human life and leads to high mortality and morbidity rates, affects millions of people, especially in subtropical and tropical regions. Because of the dependence on human skills and the inaccuracy of manual analysis, conventional diagnostic techniqu...

Deep Learning Techniques for Microscopic Identification and Detection of Plant-Parasitic Nematodes

Rapid multi-species malaria parasite detection using deep learning

Visual examination of Giemsa-stained blood smears remains the gold standard for malaria parasite detection but is labour-intensive, difficult to standardise, and a major barrier to scalable digital microscopy workflows. Towards automating smear counting and supporting digital archiving, we previously developed Plas...

Robustness enhancement of CNN-based mosquito larva species classification with out of distribution-aware augmentation and ensemble learning

Accurate identification of mosquito larvae is important for vector surveillance and early control of mosquito-borne diseases, yet classification performance often degrades under blur, acquisition variability, and cross-domain shift. This thesis presents a robustness-oriented deep learning framework for mosquito-larva s...