A machine learning-based system for detecting leishmaniasis in microscopic images

• Published in: BMC infectious diseases Vol. 22; no. 1; pp. 48 - 6
• Main Authors: Zare, Mojtaba, Akbarialiabad, Hossein, Parsaei, Hossein, Asgari, Qasem, Alinejad, Ali, Bahreini, Mohammad Saleh, Hosseini, Seyed Hossein, Ghofrani-Jahromi, Mohsen, Shahriarirad, Reza, Amirmoezzi, Yalda, Shahriarirad, Sepehr, Zeighami, Ali, Abdollahifard, Gholamreza
• Format: Journal Article
• Language: English
• Published: London BioMed Central 12.01.2022; BioMed Central Ltd; BMC
• Subjects: Adaboost; Algorithms; Amastigotes; Artificial Intelligence; Biopsy; Care and treatment; Cutaneous leishmaniasis; Diagnosis; Feature extraction; Humans; Image classification; Image processing; Infectious Diseases; Internal Medicine; Laboratories; Learning algorithms; Leishmania; Leishmaniasis; Leishmaniasis - diagnosis; Leishmaniasis, Cutaneous; Machine Learning; Macrophages; Malaria; Medical imaging; Medical Microbiology; Medicine; Medicine & Public Health; Methods; Microscopy, Medical; Morphology; Parasites; Parasitic diseases; Parasitology; Risk factors; Tropical Medicine; Vector-borne diseases; Viola-Jones; Iran; Cutaneous leishmaniasis; Viola-Jones; Image processing; Leishmania; Artificial intelligence; Algorithm; Adaboost
• ISSN: 1471-2334; 1471-2334
• DOI: 10.1186/s12879-022-07029-7

Background Leishmaniasis, 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. Methods We 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. Results A 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. Conclusion The 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.