Abstract
BackgroundLeishmaniasis, a disease caused by a protozoan, causes numerous deaths in humans each year. After malaria, leishmaniasis is known to be the deadliest parasitic disease globally. Direct visual detection of leishmania parasite through microscopy is the frequent method for diagnosis of this disease. However, this method is time-consuming and subject to errors. This study was aimed to develop an artificial intelligence-based algorithm for automatic diagnosis of leishmaniasis.MethodsWe used the Viola-Jones algorithm to develop a leishmania parasite detection system. The algorithm includes three procedures: feature extraction, integral image creation, and classification. Haar-like features are used as features. An integral image was used to represent an abstract of the image that significantly speeds up the algorithm. The adaBoost technique was used to select the discriminate features and to train the classifier.ResultsA 65% recall and 50% precision was concluded in the detection of macrophages infected with the leishmania parasite. Also, these numbers were 52% and 71%, respectively, related to amastigotes outside of macrophages.ConclusionThe developed system is accurate, fast, easy to use, and cost-effective. Therefore, artificial intelligence might be used as an alternative for the current leishmanial diagnosis methods.
Highlights
Leishmaniasis, a disease caused by a protozoan, causes numerous deaths in humans each year
We developed an artificial intelligence (AI)-based system to assist with detecting and diagnosing leishmania parasites
We used 300 images taken from 50 laboratory slides acquired from lesions suspected of leishmaniasis and from patients referred to Valfajr Clinic in Shiraz, Fars, Iran
Summary
Leishmaniasis, a disease caused by a protozoan, causes numerous deaths in humans each year. Direct visual detection of leishmania parasite through microscopy is the frequent method for diagnosis of this disease. This method is time-con‐ suming and subject to errors. Leishmaniasis, a disease caused by more than 20 species of leishmania parasites, is recognized in the tropical and subtropical regions as an acute disease with a high mortality rate. Cutaneous Leishmaniasis (CL) is endemic in more than 88 countries and around two-third of the cases occur in Afghanistan, Algeria, Brazil, Pakistan, Peru, Saudi Arabia, Iran, and Syria [3, 4]. CL is estimated to cause 1 million new cases [5], with limited responsed in treatment and management [6,7,8,9,10].
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