Handling class imbalance in COVID-19 chest X-ray images classification: Using SMOTE and weighted loss

Chamseddine, Ekram; Mansouri, Nesrine; Soui, Makram; Abed, Mourad

doi:10.1016/j.asoc.2022.109588

Cited by 42 publications

(6 citation statements)

References 65 publications

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“…In order to accomplish the classification task, we have used four different classifiers such as DT, RF, KNN, and SVM where each of which is developed with the help of the scikit-learn Python package [18] but KNN and RF outperformed the others. To overcome the imbalanced data, we have tried undersampling, oversampling, synthetic minority oversampling technique (SMOTE) and SMOTE-Tomek [19][20][21]. However, SMOTE outperforms the other data balancing techniques.…”

Section: Classifiersmentioning

confidence: 99%

Machine learning approach for detecting Covid-19 from speech signal using Mel frequency magnitude coefficient

Nayak

Darji

Shah

2023

SIViP

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The Covid-19 pandemic is one of the most significant global health concerns that have emerged in this decade. Intelligent healthcare technology and techniques based on speech signal and artificial intelligence make it feasible to provide a faster and more efficient timely detection of Covid-19. The main objective of our study is to design speech signal-based noninvasive, low-cost, remote diagnosis of Covid-19. In this study, we have developed system to detect Covid-19 from speech signal using Mel frequency magnitude coefficients (MFMC) and machine learning techniques. In order to capture higher-order spectral features, the spectrum is divided into a larger number of subbands with narrower bandwidths as MFMC, which leads to better frequency resolution and less overall noise. As a consequence of an improvement in frequency resolution as well as a decrease in the quantity of noise that is included with the extraction of MFMC, the higher-order MFMCs are able to identify Covid-19 from speech signals with an increased level of accuracy. The procedures for machine learning are often less complicated than those for deep learning, and they may commonly be carried out on regular computers. However, deep learning systems need extensive computing power and data storage. Twelve, twenty-four, thirty, and forty spectral coefficients are obtained using MFMC in our study, and from these coefficients, performance is accessed using machine learning classifiers, such as random forests and K -nearest neighbor (KNN); however, KNN has performed better than the other model with having AUC score of 0.80.

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Section: Classifiersmentioning

confidence: 99%

Machine learning approach for detecting Covid-19 from speech signal using Mel frequency magnitude coefficient

Nayak

Darji

Shah

2023

SIViP

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“…A class imbalance problem is solved for classifying COVID-19 in chest X-ray image sequences by Chamseddine et al [ 25 ]. This approach utilized SMOTE and weighted loss.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Master-Slave Architecture to Detect COVID-19 in Chest X-ray Image Sequences Using Transfer-Learning Techniques

Aljohani

Alharbe

2022

Healthcare

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Coronavirus disease, frequently referred to as COVID-19, is a contagious and transmittable disease produced by the SARS-CoV-2 virus. The only solution to tackle this virus and reduce its spread is early diagnosis. Pathogenic laboratory tests such as the polymerase chain reaction (PCR) process take a long time. Also, they regularly produce incorrect results. However, they are still considered the critical standard for detecting the virus. Hence, there is a solid need to evolve computer-assisted diagnosis systems capable of providing quick and low-cost testing in areas where traditional testing procedures are not feasible. This study focuses on COVID-19 detection using X-ray images. The prime objective is to introduce a computer-assisted diagnosis (CAD) system to differentiate COVID-19 from healthy and pneumonia cases using X-ray image sequences. This work utilizes standard transfer-learning techniques for COVID-19 detection. It proposes the master–slave architecture using the most state-of-the-art Densenet201 and Squeezenet1_0 techniques for classifying the COVID-19 virus in chest X-ray image sequences. This paper compares the proposed models with other standard transfer-learning approaches for COVID-19. The performance metrics demonstrate that the proposed approach outperforms standard transfer-learning approaches. This research also fine-tunes hyperparameters and predicts the optimized learning rate to achieve the highest accuracy in the model. After fine-tuning the learning rate, the DenseNet201 model retrieves an accuracy of 83.33%, while the fastest model is SqueezeNet1_0, which retrieves an accuracy of 80%.

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“…To raise the total amount of training instances, an alternative strategy is to oversample the minority class. This may be accomplished by simply copying already-existing instances or by creating new synthetic instances using data augmentation techniques like rotation, scaling, or the addition of noise [29] .…”

Section: Introductionmentioning

confidence: 99%

Dynamic learning for imbalanced data in learning chest X-ray and CT images

Iqbal

Qureshi

et al. 2023

Heliyon

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Handling class imbalance in COVID-19 chest X-ray images classification: Using SMOTE and weighted loss

Cited by 42 publications

References 65 publications

Machine learning approach for detecting Covid-19 from speech signal using Mel frequency magnitude coefficient

Machine learning approach for detecting Covid-19 from speech signal using Mel frequency magnitude coefficient

A Novel Master-Slave Architecture to Detect COVID-19 in Chest X-ray Image Sequences Using Transfer-Learning Techniques

Dynamic learning for imbalanced data in learning chest X-ray and CT images

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