Detection of malaria parasite in thick blood smears using deep learning

Balaram, Allam; Silparaj, Manda; Gajula, Rajender

doi:10.1016/j.matpr.2022.04.1012

Cited by 15 publications

(3 citation statements)

References 7 publications

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“…Extracting useful features in the image quickly and accurately has been perplexing to scholars. Previously, scholars manually extracted features from the image [ 14 ]. However, the process of manually extracting features was very time consuming, and the results were often not ideal.…”

Section: Methods’ Resultsmentioning

confidence: 99%

ROENet: A ResNet-Based Output Ensemble for Malaria Parasite Classification

2022

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(1) Background: People may be infected with an insect-borne disease (malaria) through the blood input of malaria-infected people or the bite of Anopheles mosquitoes. Doctors need a lot of time and energy to diagnose malaria, and sometimes the results are not ideal. Many researchers use CNN to classify malaria images. However, we believe that the classification performance of malaria parasites can be improved. (2) Methods: In this paper, we propose a novel method (ROENet) to automatically classify malaria parasite on the blood smear. The backbone of ROENet is the pre-trained ResNet-18. We use randomized neural networks (RNNs) as the classifier in our proposed model. Three RNNs are used in ROENet, which are random vector functional link (RVFL), Schmidt neural network (SNN), and extreme learning machine (ELM). To improve the performance of ROENet, the results of ROENet are the ensemble outputs from three RNNs. (3) Results: We evaluate the proposed ROENet by five-fold cross-validation. The specificity, F1 score, sensitivity, and accuracy are 96.68 ± 3.81%, 95.69 ± 2.65%, 94.79 ± 3.71%, and 95.73 ± 2.63%, respectively. (4) Conclusions: The proposed ROENet is compared with other state-of-the-art methods and provides the best results of these methods.

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Section: Methods’ Resultsmentioning

confidence: 99%

ROENet: A ResNet-Based Output Ensemble for Malaria Parasite Classification

2022

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“…In both thick and thin smear microscopic [26] images, typical techniques for image processing have been employed on these images, such as morphological operations and adaptive threshold techniques, to isolate the parasite candidates from the background. When the quality diversity of their input data increases, classical ML algorithms which rely on hand-engineered features find it more difficult to generalize [13], [19], [20], [26]. Lately, there have been noteworthy advancements in the utilization of DL algorithms for many possibilities for medical imaging, namely object recognition [27], picture segmentation and reconstruction [28], and classification tasks.…”

Section: Related Workmentioning

confidence: 99%

“…In this work, we assess the performance of several deep-learning models currently in use for the identification of malaria from microscopic [18] blood pictures. We also suggest an effective deep-learning [19] approach for the differentiation between malaria cells that are infected and those that are not. The suggested personalized, CNN-based algorithm [20] beats every DL model that has been tested.…”

Section: Introductionmentioning

confidence: 99%

Malaria cell identification using improved machine learning and modified deep learning architecture

S.,

H. D.

2024

IJEECS

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Malaria continues to be a serious problem for public health because of its occurrence in tropical and subtropical areas with inadequate healthcare systems and few resources. For prompt intervention and treatment of malaria, effective and precise diagnosis is essential. Professional pathologists examine blood smear films by hand to get a microscopic diagnosis and another way they will do a rapid antigen malaria test which produces the result of 50% accuracy. Convolutional neural network (CNN) is a type of deep learning (DL) model that has been effectively used for a variety of image recognition applications. Our suggested approach uses, improved machine learning (IML) methods like support vector machine (SVM)+principal component analysis (PCA) fit, SVM+t-distributed stochastic neighbor embedding (t-SNE) fit, and CNN architecture with an accuracy of 86.23%, 88.27%, and 97.16% accuracy respectively, to combine feature extraction, data augmentation, and modify the layers by including the SVM algorithm in the final layer of the CNN architecture. The proposed method will significantly reduce pathologists' burden by automating the identification of malaria and improving diagnosis accuracy in resourceconstrained contexts

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Enhanced Deep Convolutional Neural Network for SARS-CoV-2 Variants Classification

Awe

obura

Mwanga

et al. 2023

Preprint

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High-throughput sequencing techniques and sequence analysis have enabled the taxonomic classification of pathogens present in clinical samples. Sequencing provides an unbiased identification and systematic classification of pathogens and this is generally achieved by comparing novel sequences to pre-existing annotated reference databases. However, this approach is limited by large-scale reference databases which require considerable computational resources and skills to compare against. Alternative robust methods such as machine learning are currently employed in genome sequence analysis and classification, and it can be applied in classifying SARS-CoV-2 variants, whose continued evolution has resulted in the emergence of multiple variants.We developed a deep learning Convolutional Neural Networks-Long Short Term Memory (CNN-LSTM) model to classify dominant SARS-CoV-2 variants (omicron, delta, beta, gamma and alpha) based on gene sequences from the surface glycoprotein (spike gene). We trained and validated the model using > 26,000 SARS-CoV-2 sequences from the GISAID database. The model was evaluated using unseen 3,057 SARS-CoV-2 sequences. The model was compared to existing molecular epidemiology tool, nextclade.Our model achieved an accuracy of 98.55% on training, 99.19% on the validation and 98.41% on the test dataset. Comparing the proposed model to nextclade, the model achieved significant accuracy in classifying SARS-CoV-2 variants from unseen data. Nextclade identified the presence of recombinant strains in the evaluation data, a mechanism that the proposed model did not detect.This study provides an alternative approach to pre-existing methods employed in the classification of SARS-CoV-2 variants. Timely classification will enable effective monitoring and tracking of SARS-CoV-2 variants and inform public health policies in the control and management of the COVID-19 pandemic.

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Detection of malaria parasite in thick blood smears using deep learning

Cited by 15 publications

References 7 publications

ROENet: A ResNet-Based Output Ensemble for Malaria Parasite Classification

ROENet: A ResNet-Based Output Ensemble for Malaria Parasite Classification

Malaria cell identification using improved machine learning and modified deep learning architecture

Enhanced Deep Convolutional Neural Network for SARS-CoV-2 Variants Classification

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