Malaria detection through digital microscopic imaging using Deep Greedy Network with transfer learning

Dey, Sumagna; Nath, Pradyut; Biswas, Saptarshi; Nath, Subhrapratim; Ganguly, Ankur

doi:10.1117/1.jmi.8.5.054502

Cited by 11 publications

(8 citation statements)

References 11 publications

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“…Several studies have been conducted on malaria parasite detection, which can be summarized as follows. In [ 14 ], the authors used a deep CNN to automatically discover malaria in thin blood smear images by proposing an entire computer-aided diagnosis structure. In order to optimize the process of feature selection, they used the transfer learning technique.…”

Section: Related Workmentioning

confidence: 99%

Autokeras Approach: A Robust Automated Deep Learning Network for Diagnosis Disease Cases in Medical Images

et al. 2023

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Automated deep learning is promising in artificial intelligence (AI). However, a few applications of automated deep learning networks have been made in the clinical medical fields. Therefore, we studied the application of an open-source automated deep learning framework, Autokeras, for detecting smear blood images infected with malaria parasites. Autokeras is able to identify the optimal neural network to perform the classification task. Hence, the robustness of the adopted model is due to it not needing any prior knowledge from deep learning. In contrast, the traditional deep neural network methods still require more construction to identify the best convolutional neural network (CNN). The dataset used in this study consisted of 27,558 blood smear images. A comparative process proved the superiority of our proposed approach over other traditional neural networks. The evaluation results of our proposed model achieved high efficiency with impressive accuracy, reaching 95.6% when compared with previous competitive models.

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Section: Related Workmentioning

confidence: 99%

Autokeras Approach: A Robust Automated Deep Learning Network for Diagnosis Disease Cases in Medical Images

et al. 2023

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“…Beyond diagnosis, the field of parasitology has successfully generated multiple automated image analysis tools. This includes tools developed specifically for Apicomplexan ( Kudella et al., 2016 ; Perez-Guaita et al., 2016 ; Touquet et al., 2018 ; Fisch et al., 2019 ; Bauman et al., 2020 ; Fisch et al., 2020 ; Hung et al., 2020 ; Dey et al., 2021 ; Shaw et al., 2021 ; Yoon et al., 2021 ) and Kinetoplastid research ( Wheeler et al., 2012 ; Moon et al., 2014 ; Yazdanparast et al., 2014 ; Gomes-Alves et al., 2018 ; Moraes et al., 2019 ; Wheeler, 2020 ) and applied to a range of questions, from micrograph analysis, subcellular landmark investigation and parasite motility, to insect vector behavior. Many of these parameters are common outputs from ‘omics’ and large screen studies.…”

Section: Imagingmentioning

confidence: 99%

Paving the Way: Contributions of Big Data to Apicomplexan and Kinetoplastid Research

Kent

Briggs²,

Colon³

et al. 2022

Front. Cell. Infect. Microbiol.

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In the age of big data an important question is how to ensure we make the most out of the resources we generate. In this review, we discuss the major methods used in Apicomplexan and Kinetoplastid research to produce big datasets and advance our understanding of Plasmodium, Toxoplasma, Cryptosporidium, Trypanosoma and Leishmania biology. We debate the benefits and limitations of the current technologies, and propose future advancements that may be key to improving our use of these techniques. Finally, we consider the difficulties the field faces when trying to make the most of the abundance of data that has already been, and will continue to be, generated.

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“…In the literature, several optimizations, clustering, and classification techniques are widely used for the analysis of malaria cells, some of which are discussed in this section ( 8 , 10 – 14 ). The classification techniques used for the diagnosis of the malaria cells, in which AdaBoost ( 15 ), Naïve Bayes Tree ( 16 ), SVM ( 17 ), DT ( 18 ), and Linear Discriminant ( 19 ), classifiers are involved.…”

Section: Related Workmentioning

confidence: 99%

An optimized features selection approach based on Manta Ray Foraging Optimization (MRFO) method for parasite malaria classification

Amin

Sharif

Mallah

et al. 2022

Front. Public Health

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Malaria is a serious and lethal disease that has been reported by the World Health Organization (WHO), with an estimated 219 million new cases and 435,000 deaths globally. The most frequent malaria detection method relies mainly on the specialists who examine the samples under a microscope. Therefore, a computerized malaria diagnosis system is required. In this article, malaria cell segmentation and classification methods are proposed. The malaria cells are segmented using a color-based k-mean clustering approach on the selected number of clusters. After segmentation, deep features are extracted using pre-trained models such as efficient-net-b0 and shuffle-net, and the best features are selected using the Manta-Ray Foraging Optimization (MRFO) method. Two experiments are performed for classification using 10-fold cross-validation, the first experiment is based on the best features selected from the pre-trained models individually, while the second experiment is performed based on the selection of best features from the fusion of extracted features using both pre-trained models. The proposed method provided an accuracy of 99.2% for classification using the linear kernel of the SVM classifier. An empirical study demonstrates that the fused features vector results are better as compared to the individual best-selected features vector and the existing latest methods published so far.

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Malaria detection through digital microscopic imaging using Deep Greedy Network with transfer learning

Cited by 11 publications

References 11 publications

Autokeras Approach: A Robust Automated Deep Learning Network for Diagnosis Disease Cases in Medical Images

Autokeras Approach: A Robust Automated Deep Learning Network for Diagnosis Disease Cases in Medical Images

Paving the Way: Contributions of Big Data to Apicomplexan and Kinetoplastid Research

An optimized features selection approach based on Manta Ray Foraging Optimization (MRFO) method for parasite malaria classification

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