{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T15:52:45Z","timestamp":1786981965724,"version":"build-2736575974"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T00:00:00Z","timestamp":1695859200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T00:00:00Z","timestamp":1695859200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2024,1]]},"DOI":"10.1007\/s11517-023-02926-8","type":"journal-article","created":{"date-parts":[[2023,9,27]],"date-time":"2023-09-27T23:34:55Z","timestamp":1695857695000},"page":"195-206","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Chagas parasite classification in blood sample images using different machine learning architectures"],"prefix":"10.1007","volume":"62","author":[{"given":"Lavdie","family":"Rada","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Preet","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anabel","family":"Martin-Gonzalez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carlos","family":"Brito-Loeza","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,9,28]]},"reference":[{"key":"2926_CR1","unstructured":"World Health Organization (2022) Chagas disease (also known as American trypanosomiasis). https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/chagas-disease-(american-trypanosomiasis). [Online; Accessed 14-July-2022]"},{"issue":"2","key":"2926_CR2","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1590\/S0036-46651991000200015","volume":"33","author":"L Rey","year":"1991","unstructured":"Rey L (1991) Parasitologia. Rev Inst Med Trop Sao Paulo 33(2):39\u201349","journal-title":"Rev Inst Med Trop Sao Paulo"},{"key":"2926_CR3","unstructured":"Kirchhoff L (2010) Chagas disease (American trypanosomiasis). eMedicine"},{"key":"2926_CR4","doi-asserted-by":"publisher","unstructured":"Uc-Cetina V, Brito-Loeza C, Ruiz-Pi\u00f1a H (2015) Chagas parasite detection in blood images using Adaboost. Computational and mathematical methods in medicine 2015. https:\/\/doi.org\/10.1155\/2015\/139681","DOI":"10.1155\/2015\/139681"},{"issue":"3","key":"2926_CR5","doi-asserted-by":"publisher","first-page":"633","DOI":"10.1016\/j.cmpb.2013.07.013","volume":"112","author":"R Soberanis-Mukul","year":"2013","unstructured":"Soberanis-Mukul R, Uc-Cetina V, Brito-Loeza C, Ruiz-Pi\u00f1a H (2013) An automatic algorithm for the detection of Trypanosoma cruzi parasites in blood sample images. Comput Methods Programs Biomed 112(3):633\u2013639","journal-title":"Comput Methods Programs Biomed"},{"key":"2926_CR6","doi-asserted-by":"publisher","unstructured":"Morais MC, Silva D, Milagre MM, de Oliveira MT, Pereira T, Silva JS, Costa LDF, Minoprio P, Junior RMC, Gazzinelli R, Lana M, Nakaya HI (2022) Automatic detection of the parasite Trypanosoma cruzi in blood smears using a machine learning approach applied to mobile phone images. PeerJ. https:\/\/doi.org\/10.7717\/peerj.13470","DOI":"10.7717\/peerj.13470"},{"key":"2926_CR7","doi-asserted-by":"publisher","first-page":"1099","DOI":"10.1007\/s11517-022-02537-9","volume":"60","author":"A Ojeda-Pat","year":"2022","unstructured":"Ojeda-Pat A, Martin-Gonzalez A, Brito-Loeza C, Ruiz-Pi\u00f1a H, Ruz-Suarez D (2022) Effective residual convolutional neural network for Chagas disease parasite segmentation. Medical & Biological Engineering & Computing 60:1099\u20131110","journal-title":"Medical & Biological Engineering & Computing"},{"key":"2926_CR8","doi-asserted-by":"crossref","unstructured":"Pereira A, Pyrrho A, Vanzan D, Mazza L, Gomes JG (2019) Deep convolutional neural network applied to Chagas disease parasitemia assessment. In: Fernandes BJT, Pereira J\u00fanior A (eds.) Anais do 14 Congresso Brasileiro de Intelig\u00eancia Computacional, pp. 1\u20138. ABRICOM, Curitiba, PR","DOI":"10.21528\/CBIC2019-119"},{"key":"2926_CR9","doi-asserted-by":"crossref","unstructured":"Rajaraman S, Antani SK, Poostchi M, Silamut K, Hossain MA, Maude RJ, Jaeger S, Thoma GR (2018) Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images. PeerJ 6:4568","DOI":"10.7717\/peerj.4568"},{"issue":"21","key":"2926_CR10","doi-asserted-by":"publisher","first-page":"5","DOI":"10.5120\/7470-0475","volume":"47","author":"A George","year":"2012","unstructured":"George A, Vidyapeetham A (2012) Anomaly detection based on machine learning: dimensionality reduction using PCA and classification using SVM. International Journal of Computer Applications 47(21):5\u20138","journal-title":"International Journal of Computer Applications"},{"key":"2926_CR11","unstructured":"Chen T, He T, Benesty M, Khotilovich V, Tang Y, Cho H, Chen K et al (2015) Xgboost: extreme gradient boosting. R Package Version 0.4-2 1(4), 1\u20134"},{"key":"2926_CR12","doi-asserted-by":"publisher","unstructured":"Ho TK (1995) Random decision forests. In: Proceedings of 3rd International Conference on Document Analysis and Recognition, vol. 1, pp. 278\u20132821. https:\/\/doi.org\/10.1109\/ICDAR.1995.598994","DOI":"10.1109\/ICDAR.1995.598994"},{"key":"2926_CR13","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forest. Mach Learn 45:5\u201332. https:\/\/doi.org\/10.1023\/A:1010933404324","journal-title":"Mach Learn"},{"key":"2926_CR14","first-page":"1097","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097\u20131105","journal-title":"Adv Neural Inf Process Syst"},{"key":"2926_CR15","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431\u20133440","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"2926_CR16","unstructured":"Wang N, Li S, Gupta A, Yeung DY (2015) Transferring rich feature hierarchies for robust visual tracking. arXiv preprint arXiv:1501.04587"},{"key":"2926_CR17","doi-asserted-by":"publisher","unstructured":"Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779\u2013788. https:\/\/doi.org\/10.1109\/CVPR.2016.91","DOI":"10.1109\/CVPR.2016.91"},{"key":"2926_CR18","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"key":"2926_CR19","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255. IEEE","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"2926_CR20","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","volume":"114","author":"M Belgiu","year":"2016","unstructured":"Belgiu M, Dr\u0103gu\u0163 L (2016) Random forest in remote sensing: a review of applications and future directions. ISPRS J Photogramm Remote Sens 114:24\u201331","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"2926_CR21","doi-asserted-by":"publisher","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818\u20132826. https:\/\/doi.org\/10.1109\/CVPR.2016.308","DOI":"10.1109\/CVPR.2016.308"},{"key":"2926_CR22","doi-asserted-by":"crossref","unstructured":"Chollet F (2017) Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251\u20131258","DOI":"10.1109\/CVPR.2017.195"},{"issue":"1","key":"2926_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-021-00444-8","volume":"8","author":"L Alzubaidi","year":"2021","unstructured":"Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, Santamar\u00eda J, Fadhel MA, Al-Amidie M, Farhan L (2021) Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data 8(1):1\u201374","journal-title":"Journal of Big Data"},{"key":"2926_CR24","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778. https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"2926_CR25","doi-asserted-by":"crossref","unstructured":"Chen LC, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 801\u2013818","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"2926_CR26","unstructured":"Tang Y (2013) Deep learning using linear support vector machines. arXiv preprint arXiv:1306.0239"},{"issue":"7","key":"2926_CR27","doi-asserted-by":"publisher","first-page":"1895","DOI":"10.1162\/089976698300017197","volume":"10","author":"TG Dietterich","year":"1998","unstructured":"Dietterich TG (1998) Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput 10(7):1895\u20131923","journal-title":"Neural Comput"}],"container-title":["Medical & Biological Engineering & Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-023-02926-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-023-02926-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-023-02926-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,2]],"date-time":"2024-01-02T09:23:37Z","timestamp":1704187417000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-023-02926-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,28]]},"references-count":27,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,1]]}},"alternative-id":["2926"],"URL":"https:\/\/doi.org\/10.1007\/s11517-023-02926-8","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,28]]},"assertion":[{"value":"1 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 September 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 September 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All experimental procedures were conducted in strict adherence to the Guidelines on the Ethical Use of Animals from the Mexican Institutes of Health Research (DOF. NOM-062-Z00-1999) as well as the National Institutes of Health Guide for the Care and Use of Laboratory Animals (NIH publication No. 80-23, revised 1996).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}