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

Abstract 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...
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Autor*in:	Mojtaba Zare [verfasserIn] Hossein Akbarialiabad [verfasserIn] Hossein Parsaei [verfasserIn] Qasem Asgari [verfasserIn] Ali Alinejad [verfasserIn] Mohammad Saleh Bahreini [verfasserIn] Seyed Hossein Hosseini [verfasserIn] Mohsen Ghofrani-Jahromi [verfasserIn] Reza Shahriarirad [verfasserIn] Yalda Amirmoezzi [verfasserIn] Sepehr Shahriarirad [verfasserIn] Ali Zeighami [verfasserIn] Gholamreza Abdollahifard [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Leishmania Cutaneous leishmaniasis Artificial intelligence Image processing Adaboost Viola-Jones

Übergeordnetes Werk:	In: BMC Infectious Diseases - BMC, 2003, 22(2022), 1, Seite 6
Übergeordnetes Werk:	volume:22 ; year:2022 ; number:1 ; pages:6

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/s12879-022-07029-7

Katalog-ID:	DOAJ063426250

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520			\|a Abstract 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.
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allfields	10.1186/s12879-022-07029-7 doi (DE-627)DOAJ063426250 (DE-599)DOAJ9d6aac3afc8a48adb56e4a6af62e837e DE-627 ger DE-627 rakwb eng RC109-216 Mojtaba Zare verfasserin aut A machine learning-based system for detecting leishmaniasis in microscopic images 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract 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. Leishmania Cutaneous leishmaniasis Artificial intelligence Image processing Adaboost Viola-Jones Infectious and parasitic diseases Hossein Akbarialiabad verfasserin aut Hossein Parsaei verfasserin aut Qasem Asgari verfasserin aut Ali Alinejad verfasserin aut Mohammad Saleh Bahreini verfasserin aut Seyed Hossein Hosseini verfasserin aut Mohsen Ghofrani-Jahromi verfasserin aut Reza Shahriarirad verfasserin aut Yalda Amirmoezzi verfasserin aut Sepehr Shahriarirad verfasserin aut Ali Zeighami verfasserin aut Gholamreza Abdollahifard verfasserin aut In BMC Infectious Diseases BMC, 2003 22(2022), 1, Seite 6 (DE-627)326645381 (DE-600)2041550-3 14712334 nnns volume:22 year:2022 number:1 pages:6 https://doi.org/10.1186/s12879-022-07029-7 kostenfrei https://doaj.org/article/9d6aac3afc8a48adb56e4a6af62e837e kostenfrei https://doi.org/10.1186/s12879-022-07029-7 kostenfrei https://doaj.org/toc/1471-2334 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 1 6
spelling	10.1186/s12879-022-07029-7 doi (DE-627)DOAJ063426250 (DE-599)DOAJ9d6aac3afc8a48adb56e4a6af62e837e DE-627 ger DE-627 rakwb eng RC109-216 Mojtaba Zare verfasserin aut A machine learning-based system for detecting leishmaniasis in microscopic images 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract 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. Leishmania Cutaneous leishmaniasis Artificial intelligence Image processing Adaboost Viola-Jones Infectious and parasitic diseases Hossein Akbarialiabad verfasserin aut Hossein Parsaei verfasserin aut Qasem Asgari verfasserin aut Ali Alinejad verfasserin aut Mohammad Saleh Bahreini verfasserin aut Seyed Hossein Hosseini verfasserin aut Mohsen Ghofrani-Jahromi verfasserin aut Reza Shahriarirad verfasserin aut Yalda Amirmoezzi verfasserin aut Sepehr Shahriarirad verfasserin aut Ali Zeighami verfasserin aut Gholamreza Abdollahifard verfasserin aut In BMC Infectious Diseases BMC, 2003 22(2022), 1, Seite 6 (DE-627)326645381 (DE-600)2041550-3 14712334 nnns volume:22 year:2022 number:1 pages:6 https://doi.org/10.1186/s12879-022-07029-7 kostenfrei https://doaj.org/article/9d6aac3afc8a48adb56e4a6af62e837e kostenfrei https://doi.org/10.1186/s12879-022-07029-7 kostenfrei https://doaj.org/toc/1471-2334 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 1 6
allfields_unstemmed	10.1186/s12879-022-07029-7 doi (DE-627)DOAJ063426250 (DE-599)DOAJ9d6aac3afc8a48adb56e4a6af62e837e DE-627 ger DE-627 rakwb eng RC109-216 Mojtaba Zare verfasserin aut A machine learning-based system for detecting leishmaniasis in microscopic images 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract 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. Leishmania Cutaneous leishmaniasis Artificial intelligence Image processing Adaboost Viola-Jones Infectious and parasitic diseases Hossein Akbarialiabad verfasserin aut Hossein Parsaei verfasserin aut Qasem Asgari verfasserin aut Ali Alinejad verfasserin aut Mohammad Saleh Bahreini verfasserin aut Seyed Hossein Hosseini verfasserin aut Mohsen Ghofrani-Jahromi verfasserin aut Reza Shahriarirad verfasserin aut Yalda Amirmoezzi verfasserin aut Sepehr Shahriarirad verfasserin aut Ali Zeighami verfasserin aut Gholamreza Abdollahifard verfasserin aut In BMC Infectious Diseases BMC, 2003 22(2022), 1, Seite 6 (DE-627)326645381 (DE-600)2041550-3 14712334 nnns volume:22 year:2022 number:1 pages:6 https://doi.org/10.1186/s12879-022-07029-7 kostenfrei https://doaj.org/article/9d6aac3afc8a48adb56e4a6af62e837e kostenfrei https://doi.org/10.1186/s12879-022-07029-7 kostenfrei https://doaj.org/toc/1471-2334 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 1 6
allfieldsGer	10.1186/s12879-022-07029-7 doi (DE-627)DOAJ063426250 (DE-599)DOAJ9d6aac3afc8a48adb56e4a6af62e837e DE-627 ger DE-627 rakwb eng RC109-216 Mojtaba Zare verfasserin aut A machine learning-based system for detecting leishmaniasis in microscopic images 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract 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. Leishmania Cutaneous leishmaniasis Artificial intelligence Image processing Adaboost Viola-Jones Infectious and parasitic diseases Hossein Akbarialiabad verfasserin aut Hossein Parsaei verfasserin aut Qasem Asgari verfasserin aut Ali Alinejad verfasserin aut Mohammad Saleh Bahreini verfasserin aut Seyed Hossein Hosseini verfasserin aut Mohsen Ghofrani-Jahromi verfasserin aut Reza Shahriarirad verfasserin aut Yalda Amirmoezzi verfasserin aut Sepehr Shahriarirad verfasserin aut Ali Zeighami verfasserin aut Gholamreza Abdollahifard verfasserin aut In BMC Infectious Diseases BMC, 2003 22(2022), 1, Seite 6 (DE-627)326645381 (DE-600)2041550-3 14712334 nnns volume:22 year:2022 number:1 pages:6 https://doi.org/10.1186/s12879-022-07029-7 kostenfrei https://doaj.org/article/9d6aac3afc8a48adb56e4a6af62e837e kostenfrei https://doi.org/10.1186/s12879-022-07029-7 kostenfrei https://doaj.org/toc/1471-2334 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 1 6
allfieldsSound	10.1186/s12879-022-07029-7 doi (DE-627)DOAJ063426250 (DE-599)DOAJ9d6aac3afc8a48adb56e4a6af62e837e DE-627 ger DE-627 rakwb eng RC109-216 Mojtaba Zare verfasserin aut A machine learning-based system for detecting leishmaniasis in microscopic images 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract 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. Leishmania Cutaneous leishmaniasis Artificial intelligence Image processing Adaboost Viola-Jones Infectious and parasitic diseases Hossein Akbarialiabad verfasserin aut Hossein Parsaei verfasserin aut Qasem Asgari verfasserin aut Ali Alinejad verfasserin aut Mohammad Saleh Bahreini verfasserin aut Seyed Hossein Hosseini verfasserin aut Mohsen Ghofrani-Jahromi verfasserin aut Reza Shahriarirad verfasserin aut Yalda Amirmoezzi verfasserin aut Sepehr Shahriarirad verfasserin aut Ali Zeighami verfasserin aut Gholamreza Abdollahifard verfasserin aut In BMC Infectious Diseases BMC, 2003 22(2022), 1, Seite 6 (DE-627)326645381 (DE-600)2041550-3 14712334 nnns volume:22 year:2022 number:1 pages:6 https://doi.org/10.1186/s12879-022-07029-7 kostenfrei https://doaj.org/article/9d6aac3afc8a48adb56e4a6af62e837e kostenfrei https://doi.org/10.1186/s12879-022-07029-7 kostenfrei https://doaj.org/toc/1471-2334 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 1 6
language	English
source	In BMC Infectious Diseases 22(2022), 1, Seite 6 volume:22 year:2022 number:1 pages:6
sourceStr	In BMC Infectious Diseases 22(2022), 1, Seite 6 volume:22 year:2022 number:1 pages:6
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Leishmania Cutaneous leishmaniasis Artificial intelligence Image processing Adaboost Viola-Jones Infectious and parasitic diseases
isfreeaccess_bool	true
container_title	BMC Infectious Diseases
authorswithroles_txt_mv	Mojtaba Zare @@aut@@ Hossein Akbarialiabad @@aut@@ Hossein Parsaei @@aut@@ Qasem Asgari @@aut@@ Ali Alinejad @@aut@@ Mohammad Saleh Bahreini @@aut@@ Seyed Hossein Hosseini @@aut@@ Mohsen Ghofrani-Jahromi @@aut@@ Reza Shahriarirad @@aut@@ Yalda Amirmoezzi @@aut@@ Sepehr Shahriarirad @@aut@@ Ali Zeighami @@aut@@ Gholamreza Abdollahifard @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	326645381
id	DOAJ063426250
language_de	englisch
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callnumber-first	R - Medicine
author	Mojtaba Zare
spellingShingle	Mojtaba Zare misc RC109-216 misc Leishmania misc Cutaneous leishmaniasis misc Artificial intelligence misc Image processing misc Adaboost misc Viola-Jones misc Infectious and parasitic diseases A machine learning-based system for detecting leishmaniasis in microscopic images
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topic_title	RC109-216 A machine learning-based system for detecting leishmaniasis in microscopic images Leishmania Cutaneous leishmaniasis Artificial intelligence Image processing Adaboost Viola-Jones
topic	misc RC109-216 misc Leishmania misc Cutaneous leishmaniasis misc Artificial intelligence misc Image processing misc Adaboost misc Viola-Jones misc Infectious and parasitic diseases
topic_unstemmed	misc RC109-216 misc Leishmania misc Cutaneous leishmaniasis misc Artificial intelligence misc Image processing misc Adaboost misc Viola-Jones misc Infectious and parasitic diseases
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title	A machine learning-based system for detecting leishmaniasis in microscopic images
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title_full	A machine learning-based system for detecting leishmaniasis in microscopic images
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author_browse	Mojtaba Zare Hossein Akbarialiabad Hossein Parsaei Qasem Asgari Ali Alinejad Mohammad Saleh Bahreini Seyed Hossein Hosseini Mohsen Ghofrani-Jahromi Reza Shahriarirad Yalda Amirmoezzi Sepehr Shahriarirad Ali Zeighami Gholamreza Abdollahifard
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title_sort	machine learning-based system for detecting leishmaniasis in microscopic images
callnumber	RC109-216
title_auth	A machine learning-based system for detecting leishmaniasis in microscopic images
abstract	Abstract 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.
abstractGer	Abstract 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.
abstract_unstemmed	Abstract 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.
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title_short	A machine learning-based system for detecting leishmaniasis in microscopic images
url	https://doi.org/10.1186/s12879-022-07029-7 https://doaj.org/article/9d6aac3afc8a48adb56e4a6af62e837e https://doaj.org/toc/1471-2334
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author2	Hossein Akbarialiabad Hossein Parsaei Qasem Asgari Ali Alinejad Mohammad Saleh Bahreini Seyed Hossein Hosseini Mohsen Ghofrani-Jahromi Reza Shahriarirad Yalda Amirmoezzi Sepehr Shahriarirad Ali Zeighami Gholamreza Abdollahifard
author2Str	Hossein Akbarialiabad Hossein Parsaei Qasem Asgari Ali Alinejad Mohammad Saleh Bahreini Seyed Hossein Hosseini Mohsen Ghofrani-Jahromi Reza Shahriarirad Yalda Amirmoezzi Sepehr Shahriarirad Ali Zeighami Gholamreza Abdollahifard
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A machine learning-based system for detecting leishmaniasis in microscopic images

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