Tuberculosis Disease Diagnosis Based on an Optimized Machine Learning Model

Hrizi, Olfa; Gasmi, Karim; Ltaifa, Ibtihel Ben; Alshammari, Hamoud; Karamti, Hanen; Krichen, Moez; Ammar, Lassaad Ben; Mahmood, Mahmood A.

doi:10.1155/2022/8950243

Cited by 48 publications

(18 citation statements)

References 50 publications

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“…In the future, we will consider tuning our proposed model to perform high-performance classification tasks for other medical images such as lung cancer images and phasic dopamine releases [40]. Besides, the proposed system can be customized to provide detection in advance with high accuracy for several other health risks such as breast cancer detection [41], Tuberculosis Disease Diagnosis [42], and early-stage diabetes risk prediction [43]. Also, we will seek to develop a comparative study on the use of CNN with several other metaheuristic algorithms [20] such as particle swarm optimization (PSO) and Cuckoo Optimization Algorithm (COA) [42].…”

Section: Conclusion and Future Scopementioning

confidence: 99%

Hyper-Tuned CNN Using EVO Technique for Efficient Biomedical Image Classification

Alnabhan

Habboush

Al‐Haija

et al. 2022

Mobile Information Systems

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This research utilizes metaheuristic optimization inspired by the Egyptian Vulture Optimization (EVO) technique. Biomedical image segregation is developed to reduce the complex association of hyperparameters of Convolutional Neural networks (CNN). The complex attributes of CNN include the type of kernel, size of the kernel, size of the batch, epoch counts, momentum, learning rate, activation function, convolution layer, and dropout. However, the life cycle of an Egyptian vulture influences the optimization technique to resolve complexity and increase the accuracy of CNN. The proposed CNN-based EVO model was evaluated in comparison to ANN-based and deep learning-based classifiers utilizing brain MRI image datasets. The results achieved have confirmed the efficiency and performance of the proposed CNN-based EVO model, in which the average detection accuracy and precision were 93% and 95%, respectively.

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Section: Conclusion and Future Scopementioning

confidence: 99%

Hyper-Tuned CNN Using EVO Technique for Efficient Biomedical Image Classification

Alnabhan

Habboush

Al‐Haija

et al. 2022

Mobile Information Systems

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“…This technique's advantage is that it extracts representative features, but the disadvantage is that it does not achieve satisfactory accuracy. Olfa et al [13] developed improved SVM algorithms to classify chest X-rays based on texture features extracted by the WT method. A genetic algorithm was applied to select representative features.…”

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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“…15 The World Health Organization (WHO) has assessed that nearly a quarter of the world's population receives incorrect treatment for childhood TB, leading to the evaluation of hidden TB. 16 Using ML algorithms to predict the risks associated with TB treatment inappropriately received by children is essential for improving childhood TB management in healthcare organizations and enabling patient-specific interventions. Therefore, this article provides a novel stacked ensemble learning method to predict failed TB treatment with early identification of pediatric patients at risk.…”

Section: Introductionmentioning

confidence: 99%

“…Among TB‐infected children, potential health disorders that may arise due to unsuccessful treatment include smear positivity, 12 being HIV positive, 13 low body weight, 14 and, most importantly, death 15 . The World Health Organization (WHO) has assessed that nearly a quarter of the world's population receives incorrect treatment for childhood TB, leading to the evaluation of hidden TB 16 . Using ML algorithms to predict the risks associated with TB treatment inappropriately received by children is essential for improving childhood TB management in healthcare organizations and enabling patient‐specific interventions.…”

Section: Introductionmentioning

confidence: 99%

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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Tuberculosis Disease Diagnosis Based on an Optimized Machine Learning Model

Cited by 48 publications

References 50 publications

Hyper-Tuned CNN Using EVO Technique for Efficient Biomedical Image Classification

Hyper-Tuned CNN Using EVO Technique for Efficient Biomedical Image Classification

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

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

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