Extreme Learning Machine based Differentiation of Pulmonary Tuberculosis in Chest Radiographs using Integrated Local Feature Descriptors

Govindarajan, S.; Swaminathan, Ramakrishnan

doi:10.1016/j.cmpb.2021.106058

Cited by 19 publications

(15 citation statements)

References 38 publications

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“…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%

“…[36] Single iteration-maximum pattern margin support vector machine + probability esti-kNN c , multiple-instance learning-based system: miSVM d , miSVM + probability Radiologist's reading Zambia, Tanzania, Gambia Melendez et al [37] mation and data discarding (0. Twenty-five of the included studies [14,[17][18][19]34,[36][37][38][47][48][49]52,53,56,59,[62][63][64][65][66]68,70,[72][73][74] reported comparison results with several other previous studies, while the remaining 22 did not. Twenty-six of 47 included studies [19,20,35,[38][39][40]42,43,45,[47][48][49][50]53,55,56,[58][59][60][61][62]…”

Section: General Characteristics Of the Included Studiesmentioning

confidence: 99%

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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: Study Resultsmentioning

confidence: 99%

Section: General Characteristics Of the Included Studiesmentioning

confidence: 99%

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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“…Govindarajan and Swaminathan ( 2021 ) used reaction-diffusion set method for lung segmentation followed by local feature descriptors such as Median Robust Extended Local Binary Patterns (Liu et al, 2016 ), local binary pattern (Liu et al, 2017 ) and Gradient Local Ternary Patterns (Ahmed and Hossain, 2013 ) with Extreme Learning Machine (ELM) and Online Sequential ELM (OSELM) (Liang et al, 2006 ) classifiers for detecting TB in CXR images using Montgomery dataset. Alfadhli et al ( 2017 ) used speed-up robust features (SURF) (Bay et al, 2008 ) for feature detection and performed classification using SVM for TB diagnosis.…”

Section: Disease Detection Based Literaturementioning

confidence: 99%

AI-based radiodiagnosis using chest X-rays: A review

2023

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Chest Radiograph or Chest X-ray (CXR) is a common, fast, non-invasive, relatively cheap radiological examination method in medical sciences. CXRs can aid in diagnosing many lung ailments such as Pneumonia, Tuberculosis, Pneumoconiosis, COVID-19, and lung cancer. Apart from other radiological examinations, every year, 2 billion CXRs are performed worldwide. However, the availability of the workforce to handle this amount of workload in hospitals is cumbersome, particularly in developing and low-income nations. Recent advances in AI, particularly in computer vision, have drawn attention to solving challenging medical image analysis problems. Healthcare is one of the areas where AI/ML-based assistive screening/diagnostic aid can play a crucial part in social welfare. However, it faces multiple challenges, such as small sample space, data privacy, poor quality samples, adversarial attacks and most importantly, the model interpretability for reliability on machine intelligence. This paper provides a structured review of the CXR-based analysis for different tasks, lung diseases and, in particular, the challenges faced by AI/ML-based systems for diagnosis. Further, we provide an overview of existing datasets, evaluation metrics for different[][15mm][0mm]Q5 tasks and patents issued. We also present key challenges and open problems in this research domain.

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“…To improve the efficiency and accuracy of CNN, many researchers have developed various hybrid mechanisms. [10][11][12][13] To address the challenges of lesion segmentation, several techniques were used to optimize the segmentation algorithm. However, due to the varying depths of infection, lesion segmentation remains a challenging task.…”

Section: Introductionmentioning

confidence: 99%

Gray level fuzzy deep neural networks for enhancing performance in lung disease detection: A comparative study with fuzzy logic methods

Muthukumar,

Prasad,

Raju

et al. 2024

Int J Imaging Syst Tech

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Lung cancer is a deadly disease, and its early detection is crucial for effective treatment. In this context, accurate classification of lung cancers from computed tomography imaging is a vital research area. Irregularly detected gray matter in these images can affect classification outcomes, making accurate lung cancer detection difficult. To address this issue, researchers have developed a new approach that combines fuzzy logic and deep neural networks (DNNs) for extracting the hidden characteristic features of lung diseases from the images, and thereby, ensuring that only relevant features are used for classification. The proposed gray‐level fuzzy approach on DNNs (GL‐FDNN) is designed to accurately classify four distinguished lung cancer classes namely, Large cell carcinoma, Adenocarcinoma, Normal lung computed tomography, and Squamous cell carcinoma. After identifying the area of interest, its pixel intensity ratio is used to derive the fuzzy logic, which is then used to extract the most obscure gray matter features. Then, ResNet‐18 sorts the obscure features into categories and picks only relevant features to improve classification accuracy. The gray‐level fuzzy approach on DNNs technique was tested alongside some of the renowned, existing techniques for their relative performances. Experimental analysis was carried out on standard Kaggle datasets and the outcomes reveal that the proposed technique offers the highest level of lung disease classification accuracy (99.2%). It also improves the recall and precision factors. Thus, it can serve as a valuable diagnosis tool that can enhance the detection of lung cancer and the effectiveness of its treatment to save lives.

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Extreme Learning Machine based Differentiation of Pulmonary Tuberculosis in Chest Radiographs using Integrated Local Feature Descriptors

Cited by 19 publications

References 38 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

AI-based radiodiagnosis using chest X-rays: A review

Gray level fuzzy deep neural networks for enhancing performance in lung disease detection: A comparative study with fuzzy logic methods

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