An automatic algorithm for the detection of Trypanosoma cruzi parasites in blood sample images

Soberanis-Mukul, Roger D.; Uc-Cetina, Víctor; Brito‐Loeza, Carlos; Ruiz‐Piña, Hugo A.

doi:10.1016/j.cmpb.2013.07.013

Cited by 18 publications

(14 citation statements)

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“…Currently, there is only a couple of papers reporting results on Chagas parasites detection using machine learning methods [ 8 , 9 ]. In the former, a Gaussian discriminant analysis is implemented and the resulting performance rates are 0.0167 false-negatives, 0.1563 false-positives, 0.8437 true-negatives, and 0.9833 true-positives.…”

Section: Introductionmentioning

confidence: 99%

Chagas Parasite Detection in Blood Images Using AdaBoost

Uc-Cetina

Brito‐Loeza

Ruiz‐Piña

2015

Computational and Mathematical Methods in Medicine

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The Chagas disease is a potentially life-threatening illness caused by the protozoan parasite, Trypanosoma cruzi. Visual detection of such parasite through microscopic inspection is a tedious and time-consuming task. In this paper, we provide an AdaBoost learning solution to the task of Chagas parasite detection in blood images. We give details of the algorithm and our experimental setup. With this method, we get 100% and 93.25% of sensitivity and specificity, respectively. A ROC comparison with the method most commonly used for the detection of malaria parasites based on support vector machines (SVM) is also provided. Our experimental work shows mainly two things: (1) Chagas parasites can be detected automatically using machine learning methods with high accuracy and (2) AdaBoost + SVM provides better overall detection performance than AdaBoost or SVMs alone. Such results are the best ones known so far for the problem of automatic detection of Chagas parasites through the use of machine learning, computer vision, and image processing methods.

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Section: Introductionmentioning

confidence: 99%

Chagas Parasite Detection in Blood Images Using AdaBoost

Uc-Cetina

Brito‐Loeza

Ruiz‐Piña

2015

Computational and Mathematical Methods in Medicine

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“…In traditional machine learning approaches, the majority of methods used segmentation-based feature extraction prior to the training of computational models. Both [4] and [13] exploited differing intensities between color channels of stained microscope images to discriminate the foreground (a Trypanosoma parasite or a specific feature of a Trypanosoma parasite) from the background of a blood smear. In [4], the difference between the a and b channels of the Lab color space was leveraged to obtain the foreground.…”

Section: Related Workmentioning

confidence: 99%

“…In [4], the difference between the a and b channels of the Lab color space was leveraged to obtain the foreground. Likewise, the difference between the blue and green channels of the RGB color space was used to locate pixels belonging to the kinetoplast and the nucleus of the Trypanosoma cruzi (T. cruzi ) parasite in [13]. Combinations of thresholding techniques were then used to perform binary segmentation [4] or to select segmented sub-images of stained regions that belong to a parasite [13].…”

Section: Related Workmentioning

confidence: 99%

Automatic Detection of Trypanosomosis in Thick Blood Smears Using Image Pre-processing and Deep Learning

Jung

Anzaku

Ozbulak

et al. 2021

Intelligent Human Computer Interaction

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Trypanosomosis, which is caused by the Trypanosoma parasite, is an infectious disease that affects both humans and animals. Today, the microscopic examination of a Giemsa or Wright stained blood smear from an infected individual is the standard procedure for diagnosis because of the straightforward nature of sample preparation. Unfortunately, this method is labor-intensive and prone to error, particularly resulting in false-negative scoring when parasite levels are low during chronic infections. Automating the detection of parasites in blood smear images can overcome sensitivity limitations related to a microscopic examination. We therefore propose a deep learning approach that aims at automatically classifying microscope images in terms of parasite presence or absence. To that end, we applied a ResNet18 model using a pre-processed dataset derived from microscope videos of unstained thick blood smears, with the blood smears originating from a mouse infected with Trypanosoma brucei. Our pre-processing strategy mainly involved image cropping and the application of a thresholding algorithm for facilitating effective model training. Moreover, our thresholding approach made it possible to observe a positive correlation between the percentage of parasite-related pixels in an image and the classification effectiveness.

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“…When symptoms occur, they include prolonged fever, headache, enlargement of the liver, spleen, and lymph nodes, difficulties in breathing, muscle pain, swelling, abdominal or chest pain, and subcutaneous oedema (localized or generalized; Rassi, Rassi, & Rassi, ). During the chronic phase, the parasites are hidden mainly in the heart and digestive muscles (Soberanis‐Mukul, Uc‐Cetina, Brito‐Loeza, & Ruiz‐Piña, ). Clinical complications such as cardiac and neurological alterations, destruction of the nervous system, and the enlargement of the esophagus or colon have been recorded (WHO, ).…”

Section: Overview Of Malaria Leishmaniasis Chagas Disease and Denguementioning

confidence: 99%

Flavonoids as efficient scaffolds: Recent trends for malaria, leishmaniasis, Chagas disease, and dengue

Boniface

Ferreira

2019

Phytotherapy Research

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Endemic in 149 tropical and subtropical countries, neglected tropical diseases (NTDs) affect more than 1 billion people annually with over 500,000 deaths. Among the NTDs, some of the most severe consist of leishmaniasis, Chagas disease, and dengue. The impact of the combined NTDs closely rivals that of malaria. According to the World Health Organization, 216 million cases of malaria were reported in 2016 with 445,000 deaths. Current treatment options are associated with various limitations including widespread drug resistance, severe adverse effects, lengthy treatment duration, unfavorable toxicity profiles, and complicated drug administration procedures. Flavonoids are a class of compounds that has been the subject of considerable scientific interest. New developments of flavonoids have made promising advances for the potential treatment of malaria, leishmaniasis, Chagas disease, and dengue, with less toxicity, high efficacy, and improved bioavailability. This review summarizes the current standings of the use of flavonoids to treat malaria and neglected diseases such as leishmaniasis, Chagas disease, and dengue. Natural and synthetic flavonoids are leading compounds that can be used for developing antiprotozoal and antiviral agents. However, detailed studies on toxicity, pharmacokinetics, and mechanisms of action of these compounds are required to confirm the in vitro pharmacological claims of flavonoids for pharmaceutical applications. Highlights In the current review, we have tried to compile recent discoveries on natural and synthetic flavonoids as well as their implication in the treatment of malaria, leishmaniasis, Chagas disease, and dengue. A total of 373 (220 natural and 153 synthetic) flavonoids have been evaluated for antimalarial, antileishmanial, antichagasic, and antidengue activities. Most of these flavonoids showed promising results against the above diseases. Reports on molecular modeling of flavonoid compounds to the disease target indicated encouraging results. Flavonoids can be prospected as potential leads for drug development; however, more rigorously designed studies on toxicity and pharmacokinetics, as well as the quantitative structure–activity relationship studies of these compounds, need to be addressed.

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An automatic algorithm for the detection of Trypanosoma cruzi parasites in blood sample images

Cited by 18 publications

References 5 publications

Chagas Parasite Detection in Blood Images Using AdaBoost

Chagas Parasite Detection in Blood Images Using AdaBoost

Automatic Detection of Trypanosomosis in Thick Blood Smears Using Image Pre-processing and Deep Learning

Flavonoids as efficient scaffolds: Recent trends for malaria, leishmaniasis, Chagas disease, and dengue

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