A deep learning architecture for multi-class lung diseases classification using chest X-ray (CXR) images

Alshmrani, Goram Mufarah M.; Niu, Qiang; Jiang, Richard; Pervaiz, Haris; Elshennawy, Nada M.

doi:10.1016/j.aej.2022.10.053

Cited by 89 publications

(42 citation statements)

References 47 publications

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“…The usefulness of ML in TB diagnosis has been demonstrated by several studies. [29][30][31]45,46 Early diagnosis of TB is crucial in preventing advanced symptoms such as cough and shortness of breath, particularly in the elderly. 45 It is revealed in Reference 45 that some people can carry the TB bacteria but show no symptoms, and these people might infect others.…”

Section: Applications Of Machine Learning In Tuberculosismentioning

confidence: 99%

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Stacked ensemble modeling for improved tuberculosis treatment outcome prediction in pediatric cases

Yilmaz

2024

Concurrency and Computation

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SummaryThe promising results of ML (machine learning) methods in various disciplines have led to the frequent use of these methods in health fields such as disease diagnosis, personalized medicine, medical image‐based diagnosis, and predicting the number of deaths and cases in a pandemic. However, a neglected area in the field of healthcare is the lack of study with ML to predict treatment outcomes for tuberculosis (TB) patients, particularly children experiencing failed treatment. This need has become more apparent as the coronavirus pandemic has reversed the gains of health institutions with TB disease, especially in children. Therefore, this article conducted a study using the stacked ensemble ML method to early predict the risk for children experiencing a failed treatment outcome of TB. To fulfill this need and determine the most appropriate technique, a two‐stage methodology was followed in this work. First, predictions were obtained by combining the information gain feature selection (IGFS) approach with a variety of single‐based ML algorithms, including logistic regression (LR), deep belief neural networks (DBN), random forest (RF), and decision tree (DT). Second, the proposed method, which includes a stacked ensemble ML technique, was used. The latter model uses LR as a meta‐learner and the aforementioned single‐based ML algorithms (DBN, LR, RF, and DT). The performance results of ML models used in the two stages were compared, and the proposed model which is the combination of the stack‐based ensemble learning model and the IGFS technique provided better ROC curves, accuracy, precision, and recall results.

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Section: Applications Of Machine Learning In Tuberculosismentioning

confidence: 99%

“…The usefulness of ML in TB diagnosis has been demonstrated by several studies 29–31,45,46 . Early diagnosis of TB is crucial in preventing advanced symptoms such as cough and shortness of breath, particularly in the elderly 45 .…”

Section: Related Workmentioning

confidence: 99%

Stacked ensemble modeling for improved tuberculosis treatment outcome prediction in pediatric cases

Yilmaz

2024

Concurrency and Computation

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“…The presented network achieved 85.4% accuracy using ADAM optimizer, 50 epochs, and kernel size 3 × 3 settings. Alshmrani et al 36 presented a framework based on deep learning methods that use CXR images of TB, Normal, lung cancer, lung opacity, COVID‐19, and pneumonia. They evaluated their method on chest x‐rays of 6012 lung opacity, 21 000 lung cancer, 3513 COVID‐19, 5870 pneumonia, 10 192 Normal, and 1400 TB.…”

Section: Related Workmentioning

confidence: 99%

A deep learning‐based x‐ray imaging diagnosis system for classification of tuberculosis, COVID‐19, and pneumonia traits using evolutionary algorithm

Ali,

Khan,

Hamza

et al. 2023

Int J Imaging Syst Tech

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To aid in detection of tuberculosis, researchers have concentrated on developing computer‐aided diagnostic technologies based on x‐ray imaging. Since it generates noninvasive standard‐of‐care data, a chest x‐ray image is one of the most often used diagnostic imaging modalities in computer‐aided solutions. Due to their significant interclass similarities and low intra‐class variation abnormalities, chest x‐ray pictures continue to pose difficulty for proper diagnosis. In this paper, a novel automated framework is proposed for the classification of tuberculosis, COVID‐19, and pneumonia from chest x‐ray images using deep learning and improved optimization technique. Two pre‐trained convolutional neural network models such as EfficientB0 and ResNet50 have been utilized and fine‐tuned based on the additional layers. Both models are trained with fixed hyperparameters on the selected datasets and obtained newly trained models. A novel feature selection technique has been proposed that selects the best features. In the novel version, distance and update position formulation has been modified. The selected features are further fused using a novel technique that is based on the serial and standard deviation threshold function. The experimental process of the proposed framework is conducted on three datasets and obtained an accuracy of 98.2%, 99.0%, and 98.7%, respectively. In addition, a detailed Wilcoxon signed‐rank analysis is conducted and shows the proposed method significance performance. Based on the results, it is concluded that the proposed method accuracy is improved after the fusion process. In addition, the comparison with recent techniques shows the proposed method as more significant in terms of accuracy and precision rate.

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“…The final layer of the proposed model was modified using SVM after the weights from the pre-trained model were utilized as initial values and modified during training. VGG-19 followed by the three CNN blocks employed as feature extraction at the classification step in [21], the suggested VGG19 + CNN outperformed with a recall of 93.75%, and it performed better.…”

Section: Introductionmentioning

confidence: 97%

Ensemble deep models for covid-19 pandemic classification using chest x-ray images via different fusion techniques

Menshawy

Eid

Abdel-Kader

2023

Int. J. Adv. Intell. Informatics

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A pandemic epidemic called the coronavirus (COVID-19) has already afflicted people all across the world. Radiologists can visually detect coronavirus infection using a chest X-ray. This study examines two methods for categorizing COVID-19 patients based on chest x-rays: pure deep learning and traditional machine learning. In the first model, three deep learning classifiers' decisions are combined using two distinct decision fusion strategies (majority voting and Bayes optimal). To enhance classification performance, the second model merges the ideas of decision and feature fusion. Using the fusion procedure, feature vectors from deep learning models generate a feature set. The classification metrics of conventional machine learning classifiers were then optimized using a voting classifier. The first proposed model performs better than the second model when it concerns diagnosing binary and multiclass classification. The first model obtains an AUC of 0.998 for multi-class classification and 0.9755 for binary classification. The second model obtains a binary classification AUC of 0.9563 and a multiclass classification AUC of 0.968. The suggested models perform better than both the standard learners and state-of-the-art and state-of-the-art methods.

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A deep learning architecture for multi-class lung diseases classification using chest X-ray (CXR) images

Cited by 89 publications

References 47 publications

Stacked ensemble modeling for improved tuberculosis treatment outcome prediction in pediatric cases

Stacked ensemble modeling for improved tuberculosis treatment outcome prediction in pediatric cases

A deep learning‐based x‐ray imaging diagnosis system for classification of tuberculosis, COVID‐19, and pneumonia traits using evolutionary algorithm

Ensemble deep models for covid-19 pandemic classification using chest x-ray images via different fusion techniques

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