Detection of pneumonia in chest <scp>X‐ray</scp> images by using <scp>2D</scp> discrete wavelet feature extraction with random forest

Akgündoğdu, Abdurrahim

doi:10.1002/ima.22501

Cited by 14 publications

(10 citation statements)

References 32 publications

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“…In this study, due to the small size of the used dataset and the motivating results of using the handcrafted techniques and pre-trained models for extracting features from medical images in other published papers [8], [9], [13], [14], [29]- [31], so the features would be extracted using two different techniques: the pre-trained CheXNet deep model and a set of handcrafted descriptors. CheXNet Deep Features: CheXNet [32] is a 121-layer convolutional neural network based on the DenseNet architecture [33].…”

Section: B Proposed Methodologymentioning

confidence: 99%

Applying Different Machine Learning Techniques for Prediction of COVID-19 Severity

Sayed¹,

El-Korany

Mohammad

2021

IEEE Access

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Due to the increase in the number of patients who died as a result of the SARS-CoV-2 virus around the world, researchers are working tirelessly to find technological solutions to help doctors in their daily work. Fast and accurate Artificial Intelligence (AI) techniques are needed to assist doctors in their decisions to predict the severity and mortality risk of a patient. Early prediction of patient severity would help in saving hospital resources and decrease the continual death of patients by providing early medication actions. Currently, X-ray images are used as early symptoms in detecting COVID-19 patients. Therefore, in this research, a prediction model has been built to predict different levels of severity risks for the COVID-19 patient based on X-ray images by applying machine learning techniques. To build the proposed model, CheXNet deep pretrained model and hybrid handcrafted techniques were applied to extract features, two different methods: Principal Component Analysis (PCA) and Recursive Feature Elimination (RFE) were integrated to select the most important features, and then, six machine learning techniques were applied. For handcrafted features, the experiments proved that merging the features that have been selected by PCA and RFE together (PCA+RFE) achieved the best results with all classifiers compared with using all features or using the features selected by PCA or RFE individually. The XGBoost classifier achieved the best performance with the merged (PCA + RFE) features, where it accomplished 97% accuracy, 98% precision, 95% recall, 96% f1-score and 100% roc-auc. Also, SVM carried out the same results with some minor differences, but overall it was a good performance where it accomplished 97% accuracy, 96% precision, 95% recall, 95% f1-score and 99% roc-auc. On the other hand, for pre-trained CheXNet features, Extra Tree and SVM classifiers with RFE achieved 99.6% for all measures.

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Section: B Proposed Methodologymentioning

confidence: 99%

Applying Different Machine Learning Techniques for Prediction of COVID-19 Severity

Sayed¹,

El-Korany

Mohammad

2021

IEEE Access

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“…The model proposed by the authors achieved a decent accuracy of 95.3%, but the model did not incorporate any of the regularization techniques to overcome the issue of overfitting. The two-dimensional discrete wavelet transform approach to extract features from X-rays to differentiate a normal X-ray from pneumonia proposed in [ 22 ] achieved an accuracy of 97.11. However, the authors have not compared their approach with deep learning methods.…”

Section: Literature Surveymentioning

confidence: 99%

“…No. Dataset Methodology Accuracy Reference 1 OCT and chest X-ray images for classification 18 Layer CNN model 93.75 [ 20 ] 2 OCT and chest X-ray images for classification CNN model 95.3 [ 21 ] 3 OCT and chest X-ray images for classification 2D Wavelet Transform and Random Forest 97.11 [ 22 ] 4 OCT and chest X-ray images for classification CNN without Transfer Learning 95.31 [ 23 ] 5 Chest X-ray Multilayer Perceptron and CNN 94.4 [ 24 ] 6 OCT and chest X-ray images for classification Image Sharpening and customized CNN 97.92 [ 25 ] 7 OCT and Chest X-ray images for classification CNN and Grad-CAM 99.3 [ 26 ] 8 Chest X-ray images (pneumonia) AlexNet, ResNet18, DenseNet201 and SqueezeNet 98 [ 27 ] 9 OCT and chest X-ray images for classification VGG16, VGG19, DenseNet201, Inception_ResNet_V2, Inception_V3, Resnet50, MobileNet_V2 and Xception 96.61 [ …”

Section: Literature Surveymentioning

confidence: 99%

A multichannel EfficientNet deep learning-based stacking ensemble approach for lung disease detection using chest X-ray images

2022

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This paper proposes a multichannel deep learning approach for lung disease detection using chest X-rays. The multichannel models used in this work are EfficientNetB0, EfficientNetB1, and EfficientNetB2 pretrained models. The features from EfficientNet models are fused together. Next, the fused features are passed into more than one non-linear fully connected layer. Finally, the features passed into a stacked ensemble learning classifier for lung disease detection. The stacked ensemble learning classifier contains random forest and SVM in the first stage and logistic regression in the second stage for lung disease detection. The performance of the proposed method is studied in detail for more than one lung disease such as pneumonia, Tuberculosis (TB), and COVID-19. The performances of the proposed method for lung disease detection using chest X-rays compared with similar methods with the aim to show that the method is robust and has the capability to achieve better performances. In all the experiments on lung disease, the proposed method showed better performance and outperformed similar lung disease existing methods. This indicates that the proposed method is robust and generalizable on unseen chest X-rays data samples. To ensure that the features learnt by the proposed method is optimal, t-SNE feature visualization was shown on all three lung disease models. Overall, the proposed method has shown 98% detection accuracy for pediatric pneumonia lung disease, 99% detection accuracy for TB lung disease, and 98% detection accuracy for COVID-19 lung disease. The proposed method can be used as a tool for point-of-care diagnosis by healthcare radiologists.Journal instruction requires a city for affiliations; however, this is missing in affiliation 3. Please verify if the provided city is correct and amend if necessary.correct

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“…Long term diabetes leads to chronic failures such as kidneys, tissues, blood vessels and heart. Type I diabetes basically poses symptoms such as increased thirst, frequent urination, high blood and glucose levels Few machine learning algorithms such as support vector machine (SVM) [9], decision tree (DT) [24], logistic regression (LR) [21] and linear discriminant analysis (LDA) [5] have been reported in literature. However most widely used one is SVM and recently neural networks have shown a huge interest by the researchers because of its technical feasibility and performance.…”

Section: Diabetes Mellitusmentioning

confidence: 99%

A Review on the Detection of the Post COVID-19 Symptoms for Long Term Diseased Patients using Machine Learning Algorithms

Anitha

2022

J. Phys.: Conf. Ser.

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Long term diseases require continuous monitoring, sometimes periodic monitoring to verify if any serious concern requires an attention. In recent years, it is noticed that the COVID-19 pandemic has triggered serious concern towards the long-term diseased individuals. As the mortality rate of the COVID-19 clearly indicates that the highest percentage of deaths reflect in the individuals suffering from long term diseases such as diabetes, pneumonia, cardiovascular and acute renal failure. Though they are tested for COVID negative through conventional apparatus, it doesn’t confer that they are completely out of post consequences. Hence a periodic, if necessary continuous monitoring needs to be aided, which in current scenario is a challenging task. Hence, our current article reviews the use of machine learning algorithms to detect and diagnose pre and post COVID-19 effects on long term diseased patients.

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Detection of pneumonia in chest X‐ray images by using 2D discrete wavelet feature extraction with random forest

Cited by 14 publications

References 32 publications

Applying Different Machine Learning Techniques for Prediction of COVID-19 Severity

Applying Different Machine Learning Techniques for Prediction of COVID-19 Severity

A multichannel EfficientNet deep learning-based stacking ensemble approach for lung disease detection using chest X-ray images

A Review on the Detection of the Post COVID-19 Symptoms for Long Term Diseased Patients using Machine Learning Algorithms

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