Abstract
BackgroundMalaria is a public health problem that affects remote areas worldwide. Climate change has contributed to the problem by allowing for the survival of Anopheles in previously uninhabited areas. As such, several groups have made developing news systems for the automated diagnosis of malaria a priority.ObjectiveThe objective of this study was to develop a new, automated, mobile device-based diagnostic system for malaria. The system uses Giemsa-stained peripheral blood samples combined with light microscopy to identify the Plasmodium falciparum species in the ring stage of development.MethodsThe system uses image processing and artificial intelligence techniques as well as a known face detection algorithm to identify Plasmodium parasites. The algorithm is based on integral image and haar-like features concepts, and makes use of weak classifiers with adaptive boosting learning. The search scope of the learning algorithm is reduced in the preprocessing step by removing the background around blood cells.ResultsAs a proof of concept experiment, the tool was used on 555 malaria-positive and 777 malaria-negative previously-made slides. The accuracy of the system was, on average, 91%, meaning that for every 100 parasite-infected samples, 91 were identified correctly.ConclusionsAccessibility barriers of low-resource countries can be addressed with low-cost diagnostic tools. Our system, developed for mobile devices (mobile phones and tablets), addresses this by enabling access to health centers in remote communities, and importantly, not depending on extensive malaria expertise or expensive diagnostic detection equipment.
Highlights
Malaria is a public health problem worldwide
In 2014, 97 countries reported the continuous transmission of malaria and the World Health Organization (WHO) estimates that around 3.2 billion people are at risk of becoming infected with this disease [1]
The acronyms used in the evaluation were positive sample (P), negative sample (N), parasites correctly detected as positive (TP), correctly identified as negative objects (TN), objects that are not parasites of the Plasmodium sp (FP), and positive parasites not detected by the classifier (FN)
Summary
Malaria is a public health problem worldwide. In most of the high-burden countries, weak healthcare systems contribute to slower than average declines in malaria incidences and high rates of mortality [1]. Malaria is a public health problem that affects remote areas worldwide. Several groups have made developing news systems for the automated diagnosis of malaria a priority. Objective: The objective of this study was to develop a new, automated, mobile device-based diagnostic system for malaria. Methods: The system uses image processing and artificial intelligence techniques as well as a known face detection algorithm to identify Plasmodium parasites. Our system, developed for mobile devices (mobile phones and tablets), addresses this by enabling access to health centers in remote communities, and importantly, not depending on extensive malaria expertise or expensive diagnostic detection equipment
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