Greedy Autoaugment for classification of mycobacterium tuberculosis image via generalized deep CNN using mixed pooling based on minimum square rough entropy

Momeny, Mohammad; Neshat, Ali Asghar; Gholizadeh, Abdolmajid; Jafarnezhad, Ahad; Rahmanzadeh, Elham; Marhamati, Mahmoud; Moradi, Bagher; Ghafoorifar, Ali; Zhang, Yudong

doi:10.1016/j.compbiomed.2021.105175

Cited by 32 publications

(14 citation statements)

References 22 publications

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“…The computational result indicates that models that use data augmentation enhance the solution quality by 6.61 percent, compared to those that do not. This conclusion is consistent with Momeny et al [ 18 ], Tasci et al [ 22 ], and Shiraishi et al [ 60 ], which state that data augmentation is extremely beneficial for the data preparation of the deep learning model. Shiraishi et al [ 60 ] used chest X-ray (CXR) images provided by COVID-19 patients, and used a combination of classic data augmentation techniques and generative adversarial networks (GANs) in order to screen and classify the patients.…”

Section: Discussionsupporting

confidence: 91%

“…Step two involves using a three-tree deep convolutional neural network (DCNN) classification based on a TSCNN with a dual-poling structure to cut down on false positives. Using microscopic images, Momeny et al [ 18 ] suggested a CNN technique for classifying Mycobacterium tuberculosis. Using mixed poling instead of baseline and dropout improves the generalization, and PReLu has improved classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…Using mixed poling instead of baseline and dropout improves the generalization, and PReLu has improved classification accuracy. The model proposed by Momeny et al [ 18 ] has an accuracy of 93.4%. Lu et al [ 19 ] presented TBNet, a context-aware graph neural network for TB diagnosis.…”

Section: Introductionmentioning

confidence: 99%

“…Finally, the CARFLN network is trained as a tuberculosis detection classifier. Momeny et al [ 18 ] present a model whose accuracy and F-measure are 98.93% and 98.91%, respectively.…”

Section: Introductionmentioning

confidence: 99%

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Drug-Resistant Tuberculosis Treatment Recommendation, and Multi-Class Tuberculosis Detection and Classification Using Ensemble Deep Learning-Based System

et al. 2022

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This research develops the TB/non-TB detection and drug-resistant categorization diagnosis decision support system (TB-DRC-DSS). The model is capable of detecting both TB-negative and TB-positive samples, as well as classifying drug-resistant strains and also providing treatment recommendations. The model is developed using a deep learning ensemble model with the various CNN architectures. These architectures include EfficientNetB7, mobileNetV2, and Dense-Net121. The models are heterogeneously assembled to create an effective model for TB-DRC-DSS, utilizing effective image segmentation, augmentation, and decision fusion techniques to improve the classification efficacy of the current model. The web program serves as the platform for determining if a patient is positive or negative for tuberculosis and classifying several types of drug resistance. The constructed model is evaluated and compared to current methods described in the literature. The proposed model was assessed using two datasets of chest X-ray (CXR) images collected from the references. This collection of datasets includes the Portal dataset, the Montgomery County dataset, the Shenzhen dataset, and the Kaggle dataset. Seven thousand and eight images exist across all datasets. The dataset was divided into two subsets: the training dataset (80%) and the test dataset (20%). The computational result revealed that the classification accuracy of DS-TB against DR-TB has improved by an average of 43.3% compared to other methods. The categorization between DS-TB and MDR-TB, DS-TB and XDR-TB, and MDR-TB and XDR-TB was more accurate than with other methods by an average of 28.1%, 6.2%, and 9.4%, respectively. The accuracy of the embedded multiclass model in the web application is 92.6% when evaluated with the test dataset, but 92.8% when evaluated with a random subset selected from the aggregate dataset. In conclusion, 31 medical staff members have evaluated and utilized the online application, and the final user preference score for the web application is 9.52 out of a possible 10.

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Section: Discussionsupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Finally, the CARFLN network is trained as a tuberculosis detection classifier. Momeny et al [ 18 ] present a model whose accuracy and F-measure are 98.93% and 98.91%, respectively.…”

Section: Introductionmentioning

confidence: 99%

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Drug-Resistant Tuberculosis Treatment Recommendation, and Multi-Class Tuberculosis Detection and Classification Using Ensemble Deep Learning-Based System

et al. 2022

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“…A CNN is an effective tool for image classification, which has been used in various fields such as health, economics, and agriculture [4] , [5] , [6] , [7] , [8] , [9] , [10] . Last year, various types of CNNs were extensively used in COVID-19 detection in medical images.…”

Section: Introductionmentioning

confidence: 99%

Learning-to-augment incorporated noise-robust deep CNN for detection of COVID-19 in noisy X-ray images

Akbarimajd

Hoertel

Hussain

et al. 2022

Journal of Computational Science

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TBDLNet: A network for classifying multidrug‐resistant and drug‐sensitive tuberculosis

Zhu,

Tao,

Wang

et al. 2023

Engineering Reports

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This paper proposes applying a novel deep‐learning model, TBDLNet, to recognize CT images to classify multidrug‐resistant and drug‐sensitive tuberculosis automatically. The pre‐trained ResNet50 is selected to extract features. Three randomized neural networks are used to alleviate the overfitting problem. The ensemble of three RNNs is applied to boost the robustness via majority voting. The proposed model is evaluated by five‐fold cross‐validation. Five indexes are selected in this paper, which are accuracy, sensitivity, precision, F1‐score, and specificity. The TBDLNet achieves 0.9822 accuracy, 0.9815 specificity, 0.9823 precision, 0.9829 sensitivity, and 0.9826 F1‐score, respectively. The TBDLNet is suitable for classifying multidrug‐resistant tuberculosis and drug‐sensitive tuberculosis. It can detect multidrug‐resistant pulmonary tuberculosis as early as possible, which helps to adjust the treatment plan in time and improve the treatment effect.

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Greedy Autoaugment for classification of mycobacterium tuberculosis image via generalized deep CNN using mixed pooling based on minimum square rough entropy

Cited by 32 publications

References 22 publications

Drug-Resistant Tuberculosis Treatment Recommendation, and Multi-Class Tuberculosis Detection and Classification Using Ensemble Deep Learning-Based System

Drug-Resistant Tuberculosis Treatment Recommendation, and Multi-Class Tuberculosis Detection and Classification Using Ensemble Deep Learning-Based System

Learning-to-augment incorporated noise-robust deep CNN for detection of COVID-19 in noisy X-ray images

TBDLNet: A network for classifying multidrug‐resistant and drug‐sensitive tuberculosis

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