Neglected Tropical Diseases (NTDs) currently affect approximately 1.6 billion people worldwide, predominantly impacting populations with limited resources and access to healthcare. The study employs an interdisciplinary approach within the field of diagnostics and information technology to investigate the application of computer vision in developing diagnostic tools with the aim of fighting the spread of neglected tropical diseases (NTDs). By leveraging advancements in the field of computer vision, the research seeks to enhance diagnostic accuracy and efficiency by lowering the usage barriers of the diagnostic tool.

The research explores the feasibility of using computer vision to differentiate between various characteristics of images generated by a microscope in a diagnostic setting. The aim is to determine the most suitable method for image analysis in the diagnostic setting, comparing conventional image processing techniques, such as image filtering and color models, with Artificial Intelligence (AI)-based methods. The results revealed that the complexity of the images rendered conventional image filters and color models inadequate, highlighting the necessity of alternative methodologies, such as AI. The findings suggest that AI-based approaches are better suited to handle the intricate details and variations present in the images captured by the microscope by offering more accurate and reliable diagnostic capabilities. However, the model trained on single-labeled images required an additional technique for addressing images containing multiple characteristics, namely thresholding. Thresholds were essential for effecting the model’s final prediction to suit the specific use case. By implementing thresholds, the model could, to a higher degree, distinguish between overlapping features within the images, ensuring more accurate classification and enhancing overall performance in the diagnostic setting.

The final result presents a promising AI model that has the potential to reduce the usage

barriers of the diagnostic tool. Hence, this study represents a small step in the right direction

toward the larger goal of fighting the spread of neglected tropical diseases.