Severity detection and infection level identification of tuberculosis using deep learning

Chithra, R. S.; Jagatheeswari, P.

doi:10.1002/ima.22427

Cited by 15 publications

(7 citation statements)

References 32 publications

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“…Nine different models were used to classify TB from a vast dataset, and ChexNet was chosen as the best model for the task in [ 45 ]. A series of image processing techniques accompanied with feature extraction and crow search-based deep convolutional neural network (FC-SVNN) for classification of infection level of TB was proposed in [ 46 ]. The model achieved an accuracy of 93.5…”

Section: Literature Surveymentioning

confidence: 99%

“…Further to enhance the performance, the final output layer is replaced by stacking ensemble classifiers. Stacking is an ensemble technique that combines heterogeneous classifiers to estimate and correct their biases [ 46 ]. The member or base-level classifiers are trained using different learning algorithms and combined using a meta-level classifier.…”

Section: Proposed Multichannel Efficientnet Deep Learning-based Stack...mentioning

confidence: 99%

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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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Section: Literature Surveymentioning

confidence: 99%

Section: Proposed Multichannel Efficientnet Deep Learning-based Stack...mentioning

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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“…The systems framework achieved the best accuracy of 97.72%. Chithra et al [23] proposed an effective method for diagnosing tuberculosis using the FC-SVNN model. Colors were converted from RGB to LUV space, the lesion area was segmented by adaptive thresholding, and the most important features were extracted.…”

Section: Related Workmentioning

confidence: 99%

Deep and Hybrid Learning Technique for Early Detection of Tuberculosis Based on X-ray Images Using Feature Fusion

2022

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Tuberculosis (TB) is a fatal disease in developing countries, with the infection spreading through direct contact or the air. Despite its seriousness, the early detection of tuberculosis by means of reliable techniques can save the patients’ lives. A chest X-ray is a recommended screening technique for locating pulmonary abnormalities. However, analyzing the X-ray images to detect abnormalities requires highly experienced radiologists. Therefore, artificial intelligence techniques come into play to help radiologists to perform an accurate diagnosis at the early stages of TB disease. Hence, this study focuses on applying two AI techniques, CNN and ANN. Furthermore, this study proposes two different approaches with two systems each to diagnose tuberculosis from two datasets. The first approach hybridizes two CNN models, which are Res-Net-50 and GoogLeNet techniques. Prior to the classification stage, the approach applies the principal component analysis (PCA) algorithm to reduce the features’ dimensionality, aiming to extract the deep features. Then, the SVM algorithm is used for classifying features with high accuracy. This hybrid approach achieved superior results in diagnosing tuberculosis based on X-ray images from both datasets. In contrast, the second approach applies artificial neural networks (ANN) based on the fused features extracted by ResNet-50 and GoogleNet models and combines them with the features extracted by the gray level co-occurrence matrix (GLCM), discrete wavelet transform (DWT) and local binary pattern (LBP) algorithms. ANN achieved superior results for the two tuberculosis datasets. When using the first dataset, the ANN, with ResNet-50, GLCM, DWT and LBP features, achieved an accuracy of 99.2%, a sensitivity of 99.23%, a specificity of 99.41%, and an AUC of 99.78%. Meanwhile, with the second dataset, ANN, with the features of ResNet-50, GLCM, DWT and LBP, reached an accuracy of 99.8%, a sensitivity of 99.54%, a specificity of 99.68%, and an AUC of 99.82%. Thus, the proposed methods help doctors and radiologists to diagnose tuberculosis early and increase chances of survival.

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“…Herein Cognitive behavior and decision-making can be improved using deep learning (DL) and artificial intelligence (AI). Existing methods focused on detecting Covid-19 [10][11][12] from CT images, approached in [13,14] focused on Lung cancer detection, approaches in [15,16] focused on pneumonia detection, [17] focused on tuberculosis detection. Above mentioned techniques are only concentrated on single or 2 domain diseases due to the limited storage facility.…”

Section: Introductionmentioning

confidence: 99%

Advance IoT Intelligent Healthcare System for Lung Disease Classification Using Ensemble Techniques

Prabakaran¹,

Selvaraj²

2023

Computer Systems Science and Engineering

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In healthcare systems, the Internet of Things (IoT) innovation and development approached new ways to evaluate patient data. A cloud-based platform tends to process data generated by IoT medical devices instead of high storage, and computational hardware. In this paper, an intelligent healthcare system has been proposed for the prediction and severity analysis of lung disease from chest computer tomography (CT) images of patients with pneumonia, Covid-19, tuberculosis (TB), and cancer. Firstly, the CT images are captured and transmitted to the fog node through IoT devices. In the fog node, the image gets modified into a convenient and efficient format for further processing. advanced encryption Standard (AES) algorithm serves a substantial role in IoT and fog nodes for preventing data from being accessed by other operating systems. Finally, the preprocessed image can be classified automatically in the cloud by using various transfer and ensemble learning models. Herein different pre-trained deep learning architectures (Inception-ResNet-v2, VGG-19, ResNet-50) used transfer learning is adopted for feature extraction. The softmax of heterogeneous base classifiers assists to make individual predictions. As a meta-classifier, the ensemble approach is employed to obtain final optimal results. Disease predicted image is consigned to the recurrent neural network with long short-term memory (RNN-LSTM) for severity analysis, and the patient is directed to seek therapy based on the outcome. The proposed method achieved 98.6% accuracy, 0.978 precision, 0.982 recalls, and 0.974 F1-score on five class classifications. The experimental findings reveal that the proposed framework assists medical experts with lung disease screening and provides a valuable second perspective.

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Severity detection and infection level identification of tuberculosis using deep learning

Cited by 15 publications

References 32 publications

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

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

Deep and Hybrid Learning Technique for Early Detection of Tuberculosis Based on X-ray Images Using Feature Fusion

Advance IoT Intelligent Healthcare System for Lung Disease Classification Using Ensemble Techniques

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