Mobile Bot Application for Identification of Trypanosoma evansi Infection through Thin-Blood Film Examination Based on Deep Learning Approach

Jomtarak, Rangsan; Kittichai, Veerayuth; Kaewthamasorn, Morakot; Thanee, Suchansa; Arnuphapprasert, Apinya; Naing, Kaung Myat; Tongloy, Teerawat; Boonsang, Siridech; Chuwongin, Santhad

doi:10.1109/icci57424.2023.10112327

Cited by 2 publications

(2 citation statements)

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“…curve of 0.964 and a precision-recall curve of 0.962. This breakthrough offers considerable potential for using thin-blood film evaluation to diagnose T. evansi infection [38].…”

Section: Methodsmentioning

confidence: 99%

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Assessment of Deep Learning Models for Cutaneous Leishmania Parasite Diagnosis Using Microscopic Images

Abdelmula,

Mirzaei,

Güler

et al. 2023

Diagnostics

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Cutaneous leishmaniasis (CL) is a common illness that causes skin lesions, principally ulcerations, on exposed regions of the body. Although neglected tropical diseases (NTDs) are typically found in tropical areas, they have recently become more common along Africa’s northern coast, particularly in Libya. The devastation of healthcare infrastructure during the 2011 war and the following conflicts, as well as governmental apathy, may be causal factors associated with this catastrophic event. The main objective of this study is to evaluate alternative diagnostic strategies for recognizing amastigotes of cutaneous leishmaniasis parasites at various stages using Convolutional Neural Networks (CNNs). The research is additionally aimed at testing different classification models employing a dataset of ultra-thin skin smear images of Leishmania parasite-infected people with cutaneous leishmaniasis. The pre-trained deep learning models including EfficientNetB0, DenseNet201, ResNet101, MobileNetv2, and Xception are used for the cutaneous leishmania parasite diagnosis task. To assess the models’ effectiveness, we employed a five-fold cross-validation approach to guarantee the consistency of the models’ outputs when applied to different portions of the full dataset. Following a thorough assessment and contrast of the various models, DenseNet-201 proved to be the most suitable choice. It attained a mean accuracy of 0.9914 along with outstanding results for sensitivity, specificity, positive predictive value, negative predictive value, F1-score, Matthew’s correlation coefficient, and Cohen’s Kappa coefficient. The DenseNet-201 model surpassed the other models based on a comprehensive evaluation of these key classification performance metrics.

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“…curve of 0.964 and a precision-recall curve of 0.962. This breakthrough offers considerable potential for using thin-blood film evaluation to diagnose T. evansi infection [38].…”

Section: Methodsmentioning

confidence: 99%

“…CIRA Bot, the simulation platform, produced results equivalent to those of computational trials, with an area under the ROC curve of 0.964 and a precision-recall curve of 0.962. This breakthrough offers considerable potential for using thin-blood film evaluation to diagnose T. evansi infection [38].…”

Section: Trypanosoma Parasitesmentioning

confidence: 99%