Image analysis and machine learning for detecting malaria

Poostchi, Mahdieh; Silamut, Kamolrat; Maude, Richard J.; Jaeger, Stefan; Thoma, George R.

doi:10.1016/j.trsl.2017.12.004

Cited by 417 publications

(280 citation statements)

References 89 publications

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“…It is customary to provide a comparison of results for a proposed method vs other methods in the literature. This is problematic here because, as discussed in [9] most studies do not give patient-level results, due to data limitations and/or chosen methodologies. For example, if a method used a train/val split that allows a sample's objects into both train and validation, or if it did not report patient-level results, then its results are not comparable to ours.…”

Section: B Comparison To Other Methodsmentioning

confidence: 99%

Fully-automated patient-level malaria assessment on field-prepared thin blood film microscopy images

Delahunt

Gebrehiwot

Wilson

et al. 2019

2019 IEEE Global Humanitarian Technology Conference (GHTC)

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Malaria is a life-threatening disease affecting millions. Microscopy-based assessment of thin blood films is a standard method to (i) determine malaria species and (ii) quantitate high-parasitemia infections. Full automation of malaria microscopy by machine learning (ML) is a challenging task because field-prepared slides vary widely in quality and presentation, and artifacts often heavily outnumber relatively rare parasites.In this work, we describe a complete, fully-automated framework for thin film malaria analysis that applies ML methods, including convolutional neural nets (CNNs), trained on a large and diverse dataset of field-prepared thin blood films. Quantitation and species identification results are close to sufficiently accurate for the concrete needs of drug resistance monitoring and clinical use-cases on field-prepared samples.We focus our methods and our performance metrics on the field use-case requirements. We discuss key issues and important metrics for the application of ML methods to malaria microscopy.

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Section: B Comparison To Other Methodsmentioning

confidence: 99%

Fully-automated patient-level malaria assessment on field-prepared thin blood film microscopy images

Delahunt

Gebrehiwot

Wilson

et al. 2019

2019 IEEE Global Humanitarian Technology Conference (GHTC)

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“…Usually, the initial step of CAD is the acquisition of digital images of blood smears. This initial step breaks down the different approaches for the differenty types of microscopy, blood slides (thin or thick) and staining [14].…”

Section: D) Rapid Diagnostic Tests (Rdt)mentioning

confidence: 99%

Detection of malaria parasites in thick blood smear: A review

Azif

Nugroho²,

Wibirama³

2018

CST

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Based on data from World Health Organization, in 2015, there are 90% of deaths caused by malaria disease in Africa, Southeast Asia and countries of eastern Mediterranean. It makes the malaria become one of the most dangerous diseases that often leads to death. To support the diagnosis of malaria, early detection of plasmodium parasite is needed. Recently, malaria diagnosis process can be done with the help of computer, or often referred to as Computer Aided Diagnosis (CAD). By utilizing the digital image from the blood staining process, digital image processing can be performed to detect the presence of malaria parasite. There are 2 types of blood smear images that can be used in the malaria diagnosis process, namely, thin blood smear images and thick blood smear images. This paper provides a review of the techniques and methods used in the diagnosis of computer-assisted malaria using thick blood smear images as a diagnostic material.

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“…As a result, the algorithms used were revealed that CNN's RAN model was not used and this model was preferred in our study Algorithms such as Thresholding (IM), KNN, BN, Decision tree, Convolutional Neural Network (CNN) were used in these processes, also a new method has been established to identify the presence of malaria parasites image processing techniques and Support Vector Machine (SVM) and using (ANN) algorithms were used to classification malaria diseases moreover, VGG16-SVM outperforms existing CNN models in all performance indicators such as accuracy, precision, specificity and precision. As a result, the algorithms used were revealed that CNN's RAN model was not used and this model was preferred in our study [5][6][7][8][9][10][29][30]. However, to the best of our knowledge, no attention mechanisms have been applied to building feed forward networks to achieve the most advanced results in image classification tasks.…”

Section: Introductionmentioning

confidence: 99%

Detection of Malaria Diseases with Residual Attention Network

Qanbar¹,

Tasdemir²

2019

ijisae

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To describe a model using classic machine learning techniques for creating machine learning systems, a person who specializes in this technique needs to extract feature vectors. This period also breaks into expert time. Also, these methods could not process raw data without preprocessing and expert assistance. Deep learning has made great progress in solving problems at this point, and machine learning research has continued for many years. Unlike traditional machine learning and image processing techniques, deep networks enable learning processes using raw data. In this study, a deep learning approach for the classification and diagnosis of malaria is developed. For this purpose, Residual Attention Network (RAN) a deep learning Convolutional Neural Network (CNN) technique was used with previously classified datasets. The goal is to design computer-aided software for classifying blood cell images (blood samples) as "parasitized" or "uninfected". In the program, a decision support system was implemented by a deep learning approach. As a result, the RAN model achieved the best ability to produce better results in processing and classification images compared to other algorithm types. RAN model's training simulation results showed a 95.79% classification accuracy rate. Using the Support Vector Machine (SVM) obtained only 83.30% classification accuracy rate. Besides, it is evaluated that for the classification of blood cell images and diagnosis of malaria using deep learning methods can be used successfully. In addition, deep learning methods have the advantage of automatically learning features from input data and require minimal input by specialists in automated malaria diagnosis.

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Image analysis and machine learning for detecting malaria

Cited by 417 publications

References 89 publications

Fully-automated patient-level malaria assessment on field-prepared thin blood film microscopy images

Fully-automated patient-level malaria assessment on field-prepared thin blood film microscopy images

Detection of malaria parasites in thick blood smear: A review

Detection of Malaria Diseases with Residual Attention Network

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