Chagas parasite classification in blood sample images using different machine learning architectures

• Published in: Medical & biological engineering & computing Vol. 62; no. 1; pp. 195 - 206
• Main Authors: Rada, Lavdie, Kumar, Preet, Martin-Gonzalez, Anabel, Brito-Loeza, Carlos
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2024; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Bioengineering; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Blood; blood sampling; Chagas disease; Classification; Computer Applications; data collection; Datasets; Deep learning; Engineering; Feature extraction; Human Physiology; Imaging; Infections; Latin America; Learning algorithms; Machine learning; Medical imaging; Medical screening; mortality; Original Article; Parasites; Protozoa; Radiology; Support vector machines; Tropical diseases; Latin America
• ISSN: 0140-0118; 1741-0444; 1741-0444
• DOI: 10.1007/s11517-023-02926-8

Chagas disease is a life-threatening illness mainly found in Latin America. Early identification and diagnosis of Chagas disease are critical for reducing the death rate of individuals since cures and treatments are available at the acute stage. In this work, we test and compare several deep learning classification models on smear blood sample images for the task of Chagas parasite classification. Our experiments showed that the best classification model is a deep learning architecture based on a residual network together with separable convolution blocks as feature extractors and using a support vector machine algorithm as the classifier in the final layer. This optimized model, we named Res2_SVM, with a reduced number of parameters, achieved an accuracy of 98.48 % , precision of 100.0 % , recall of 97.20 % , and F1-score of 98.58 % on our test dataset, overcoming other machine learning models.