Automatic detection of the parasite Trypanosoma cruzi in blood smears using a machine learning approach applied to mobile phone images - Archive ouverte HAL
Article Dans Une Revue PeerJ Année : 2022

Automatic detection of the parasite Trypanosoma cruzi in blood smears using a machine learning approach applied to mobile phone images

Résumé

Chagas disease is a life-threatening illness caused by the parasite Trypanosoma cruzi . The diagnosis of the acute form of the disease is performed by trained microscopists who detect parasites in blood smear samples. Since this method requires a dedicated high-resolution camera system attached to the microscope, the diagnostic method is more expensive and often prohibitive for low-income settings. Here, we present a machine learning approach based on a random forest (RF) algorithm for the detection and counting of T. cruzi trypomastigotes in mobile phone images. We analyzed micrographs of blood smear samples that were acquired using a mobile device camera capable of capturing images in a resolution of 12 megapixels. We extracted a set of features that describe morphometric parameters (geometry and curvature), as well as color, and texture measurements of 1,314 parasites. The features were divided into train and test sets (4:1) and classified using the RF algorithm. The values of precision, sensitivity, and area under the receiver operating characteristic (ROC) curve of the proposed method were 87.6%, 90.5%, and 0.942, respectively. Automating image analysis acquired with a mobile device is a viable alternative for reducing costs and gaining efficiency in the use of the optical microscope.
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hal-04627513 , version 1 (28-06-2024)

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Mauro César Cafundó Morais, Diogo Silva, Matheus Marques Milagre, Maykon Tavares De Oliveira, Thaís Pereira, et al.. Automatic detection of the parasite Trypanosoma cruzi in blood smears using a machine learning approach applied to mobile phone images. PeerJ, 2022, 10, pp.e13470. ⟨10.7717/peerj.13470⟩. ⟨hal-04627513⟩

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