An Effective Vision Based Framework for the Identification of Tuberculosis in Chest X-Ray Images

Ghanshala, Tejasvi; Tripathi, Vikas; Pant, Bhaskar

doi:10.1007/978-981-15-6634-9_4

Cited by 3 publications

(9 citation statements)

References 14 publications

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Section: Risk Of Bias Assessment Resultsmentioning

confidence: 99%

“…Various ML and DL methods have been applied in the included studies: 7/47 (15%) studies [13,14,37,40,43,63,67] focused on using ML approaches, while 34/47 (72%) studies [17][18][19][20][34][35][36]39,41,[44][45][46][47][48][49][50][51][52][53][54][55][56][58][59][60][61][62]65,66,[69][70][71][72]74] used DL approaches; 4/47 (9%) studies [38,64,68,73] used both ML and DL approaches, while 2/47 (4%) [42,57] focused on industrial-grade DL image analysis software and various deep AI models without further information on the types of AI techniques used.…”

Section: Study Resultsmentioning

confidence: 99%

“…Figure 7 shows the individual performance results for each ML and DL approach. In the meta-analysis phase, we analyzed 10 of the included studies [13,17,34,36,39,50,54,63,66,70] that provided confusion matrix results. Among those 10 included studies, 2 used ML approaches, such as SVM, RF, kNN [13], extreme learning machine (ELM), and online sequential ELM [63], while 8 [17,34,36,39,50,54,66,70] used various DL methods, particularly CNN architectures, such as AlexNet, GoogLeNet, VGG16, DenseNet, ResNet, and MobileNet to name a few.…”

Section: Study Resultsmentioning

confidence: 99%

“…Figures 2 - 3 illustrate the QUADAS-2 assessment results regarding the risk of bias and applicability concerns of included studies. There were 2 studies [ 13 , 71 ] that have a high risk of bias in terms of “patient selection.” This is mainly due to incomplete information on data selection. One study [ 45 ] was identified to have a high risk of bias in terms of “index test” due to missing mandatory information of model architecture and hyperparameters being deployed in the study.…”

Section: Resultsmentioning

confidence: 99%

“…The overall accuracy of the proposed NN model was more than 94%. Ghanshala et al [ 13 ] compared various ML techniques, including support vector machine (SVM), k-nearest neighbor (kNN), random forest (RF), and NN for effective identification of TB. From their experimental results, they found that the NN classifier performed better than other classifiers to detect TB with an accuracy of 80.45%.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Machine and Deep Learning for Tuberculosis Detection on Chest X-Rays: Systematic Literature Review

Hansun¹,

Argha²,

Liaw³

et al. 2023

J Med Internet Res

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Background Tuberculosis (TB) was the leading infectious cause of mortality globally prior to COVID-19 and chest radiography has an important role in the detection, and subsequent diagnosis, of patients with this disease. The conventional experts reading has substantial within- and between-observer variability, indicating poor reliability of human readers. Substantial efforts have been made in utilizing various artificial intelligence–based algorithms to address the limitations of human reading of chest radiographs for diagnosing TB. Objective This systematic literature review (SLR) aims to assess the performance of machine learning (ML) and deep learning (DL) in the detection of TB using chest radiography (chest x-ray [CXR]). Methods In conducting and reporting the SLR, we followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A total of 309 records were identified from Scopus, PubMed, and IEEE (Institute of Electrical and Electronics Engineers) databases. We independently screened, reviewed, and assessed all available records and included 47 studies that met the inclusion criteria in this SLR. We also performed the risk of bias assessment using Quality Assessment of Diagnostic Accuracy Studies version 2 (QUADAS-2) and meta-analysis of 10 included studies that provided confusion matrix results. Results Various CXR data sets have been used in the included studies, with 2 of the most popular ones being Montgomery County (n=29) and Shenzhen (n=36) data sets. DL (n=34) was more commonly used than ML (n=7) in the included studies. Most studies used human radiologist’s report as the reference standard. Support vector machine (n=5), k-nearest neighbors (n=3), and random forest (n=2) were the most popular ML approaches. Meanwhile, convolutional neural networks were the most commonly used DL techniques, with the 4 most popular applications being ResNet-50 (n=11), VGG-16 (n=8), VGG-19 (n=7), and AlexNet (n=6). Four performance metrics were popularly used, namely, accuracy (n=35), area under the curve (AUC; n=34), sensitivity (n=27), and specificity (n=23). In terms of the performance results, ML showed higher accuracy (mean ~93.71%) and sensitivity (mean ~92.55%), while on average DL models achieved better AUC (mean ~92.12%) and specificity (mean ~91.54%). Based on data from 10 studies that provided confusion matrix results, we estimated the pooled sensitivity and specificity of ML and DL methods to be 0.9857 (95% CI 0.9477-1.00) and 0.9805 (95% CI 0.9255-1.00), respectively. From the risk of bias assessment, 17 studies were regarded as having unclear risks for the reference standard aspect and 6 studies were regarded as having unclear risks for the flow and timing aspect. Only 2 included studies had built applications based on the proposed solutions. Conclusions Findings from this SLR confirm the high potential of both ML and DL for TB detection using CXR. Future studies need to pay a close attention on 2 aspects of risk of bias, namely, the reference standard and the flow and timing aspects. Trial Registration PROSPERO CRD42021277155; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=277155

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Section: Risk Of Bias Assessment Resultsmentioning

confidence: 99%

Section: Study Resultsmentioning

confidence: 99%

Section: Study Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Machine and Deep Learning for Tuberculosis Detection on Chest X-Rays: Systematic Literature Review

Hansun¹,

Argha²,

Liaw³

et al. 2023

J Med Internet Res

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Development of Anatomy Learning System based on Augmented Reality

Bhatla

Tripathi

2021

2021 8th International Conference on Signal Processing and Integrated Networks (SPIN)

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Machine and Deep Learning for Tuberculosis Detection on Chest X-Rays: Systematic Literature Review (Preprint)

Hansun¹,

Argha²,

Liaw³

et al. 2022

Preprint

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BACKGROUND Tuberculosis (TB) was the leading infectious cause of mortality globally prior to COVID-19 and chest radiography has an important role in the detection, and subsequent diagnosis, of patients with this disease. The conventional experts reading has substantial within- and between-observer variability, indicating poor reliability of human readers. Substantial efforts have been made in utilizing various artificial intelligence–based algorithms to address the limitations of human reading of chest radiographs for diagnosing TB. OBJECTIVE This systematic literature review (SLR) aims to assess the performance of machine learning (ML) and deep learning (DL) in the detection of TB using chest radiography (chest x-ray [CXR]). METHODS In conducting and reporting the SLR, we followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A total of 309 records were identified from Scopus, PubMed, and IEEE (Institute of Electrical and Electronics Engineers) databases. We independently screened, reviewed, and assessed all available records and included 47 studies that met the inclusion criteria in this SLR. We also performed the risk of bias assessment using Quality Assessment of Diagnostic Accuracy Studies version 2 (QUADAS-2) and meta-analysis of 10 included studies that provided confusion matrix results. RESULTS Various CXR data sets have been used in the included studies, with 2 of the most popular ones being Montgomery County (n=29) and Shenzhen (n=36) data sets. DL (n=34) was more commonly used than ML (n=7) in the included studies. Most studies used human radiologist’s report as the reference standard. Support vector machine (n=5), k-nearest neighbors (n=3), and random forest (n=2) were the most popular ML approaches. Meanwhile, convolutional neural networks were the most commonly used DL techniques, with the 4 most popular applications being ResNet-50 (n=11), VGG-16 (n=8), VGG-19 (n=7), and AlexNet (n=6). Four performance metrics were popularly used, namely, accuracy (n=35), area under the curve (AUC; n=34), sensitivity (n=27), and specificity (n=23). In terms of the performance results, ML showed higher accuracy (mean ~93.71%) and sensitivity (mean ~92.55%), while on average DL models achieved better AUC (mean ~92.12%) and specificity (mean ~91.54%). Based on data from 10 studies that provided confusion matrix results, we estimated the pooled sensitivity and specificity of ML and DL methods to be 0.9857 (95% CI 0.9477-1.00) and 0.9805 (95% CI 0.9255-1.00), respectively. From the risk of bias assessment, 17 studies were regarded as having unclear risks for the reference standard aspect and 6 studies were regarded as having unclear risks for the flow and timing aspect. Only 2 included studies had built applications based on the proposed solutions. CONCLUSIONS Findings from this SLR confirm the high potential of both ML and DL for TB detection using CXR. Future studies need to pay a close attention on 2 aspects of risk of bias, namely, the reference standard and the flow and timing aspects. CLINICALTRIAL PROSPERO CRD42021277155; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=277155

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An Effective Vision Based Framework for the Identification of Tuberculosis in Chest X-Ray Images

Cited by 3 publications

References 14 publications

Machine and Deep Learning for Tuberculosis Detection on Chest X-Rays: Systematic Literature Review

Machine and Deep Learning for Tuberculosis Detection on Chest X-Rays: Systematic Literature Review

Development of Anatomy Learning System based on Augmented Reality

Machine and Deep Learning for Tuberculosis Detection on Chest X-Rays: Systematic Literature Review (Preprint)

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