Automated TB classification using ensemble of deep architectures

Hooda, Rahul; Mittal, Ajay; Sofat, Sanjeev

doi:10.1007/s11042-019-07984-5

Cited by 41 publications

(27 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main challenge posed by methods of detection of lesions is that they can give rise to multiple false positives while lacking a good proportion of true positive ones . For tuberculosis detection using deep learning methods applied in [ 53 , 57 , 58 , 91 , 119 ]. Pulmonary nodule detection using deep learning has been successfully applied in [ 82 , 108 , 136 , 157 ].…”

Section: Discussionmentioning

confidence: 99%

“… ROC S.Hwang et al 2016 [ 57 ] KIT, MC, and Shenzhen Deep CNN The first deep CNN-based Tuberculosis screening system with transfer learning technique AUC Rajpurkar et al 2017 [ 122 ] ChestX-ray14 CNN Detects Pneumonia using CheXNet is a 121-layer CNN from a chest X-ray image. F1 score Lopes & Valiati 2017 [ 91 ] Shenzhen and Montgomery CNN Comparative analysis of Pre-trained CNN as feature extractors for tuberculosis detection Accuracy, ROC Mittal et al 2018 [ 99 ] JSRT LF-SegNet Segmentation of lung field from CXR images using Fully convolutional encoder-decoder network Accuracy E.J.Hwang et al 2019 [ 58 ] 57,481 CXR images CNN Deep learning-based automatic detection (DLAD) algorithm for tuberculosis detection on CXR ROC Souza et al 2019 [ 148 ] Montgomery CNN Segmentation of lungs in CXR for detection and diagnosis of pulmonary diseases using two CNN architecture Dice coefficient Hooda et al [ 53 ] Shenzhen, Montgomery , Belarus, JSRT CNN An ensemble of three pre-trained architectures ResNet, AlexNet, and GoogleNet for TB detection Accuracy, ROC Xu et al 2019 [ 181 ] chest X-ray14 CNN, CXNet-m1 Design a hierarchical CNN structure for a new network CXNet-m1 to detect anomaly of chest X-ray images Accuracy, F1-score, and AUC Murphy et al 2019 [ 103 ] 5565 CXR images Deep learning-based CAD4TB software evaluation ROC Rajaraman and Antani 2020 [ 119 ] RSNA, Pediatric pneumonia, and Indiana, CNN An ensemble of modality-specific deep learning models for Tuberculosis (TB) detection from CXR Accuracy, AUC, C...…”

Section: Use Of Deep Learning In Medical Imagingmentioning

confidence: 99%

See 1 more Smart Citation

Medical image analysis based on deep learning approach

Puttagunta

Ravi

2021

Multimed Tools Appl

188

View full text Add to dashboard Cite

Medical imaging plays a significant role in different clinical applications such as medical procedures used for early detection, monitoring, diagnosis, and treatment evaluation of various medical conditions. Basicsof the principles and implementations of artificial neural networks and deep learning are essential for understanding medical image analysis in computer vision. Deep Learning Approach (DLA) in medical image analysis emerges as a fast-growing research field. DLA has been widely used in medical imaging to detect the presence or absence of the disease. This paper presents the development of artificial neural networks, comprehensive analysis of DLA, which delivers promising medical imaging applications. Most of the DLA implementations concentrate on the X-ray images, computerized tomography, mammography images, and digital histopathology images. It provides a systematic review of the articles for classification, detection, and segmentation of medical images based on DLA. This review guides the researchers to think of appropriate changes in medical image analysis based on DLA.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Use Of Deep Learning In Medical Imagingmentioning

confidence: 99%

Medical image analysis based on deep learning approach

Puttagunta

Ravi

2021

Multimed Tools Appl

188

View full text Add to dashboard Cite

show abstract

“…An ensemble method using the weighted averages of the probability scores for the AlexNet and GoogLeNet algorithms was used by Lakhani and Sundaram [ 57 ]. In [ 79 ], ensemble by weighted averages of probability scores is used. An ensemble of six CNNs was developed by Islam et al [ 71 ].…”

Section: The Taxonomy Of State-of-the-art Work On Lung Disease Detection Using Deep Learningmentioning

confidence: 99%

“…Features were extracted using the RID network, and SVM was used as a classifier. Tuberculosis classification was also executed using another ensemble of three regular architectures: ResNet, AlexNet and GoogleNet [ 79 ]. Each architecture was trained from scratch, and different optimal hyper-parameter values were used.…”

Section: The Taxonomy Of State-of-the-art Work On Lung Disease Detection Using Deep Learningmentioning

confidence: 99%

A Survey of Deep Learning for Lung Disease Detection on Medical Images: State-of-the-Art, Taxonomy, Issues and Future Directions

et al. 2020

View full text Add to dashboard Cite

The recent developments of deep learning support the identification and classification of lung diseases in medical images. Hence, numerous work on the detection of lung disease using deep learning can be found in the literature. This paper presents a survey of deep learning for lung disease detection in medical images. There has only been one survey paper published in the last five years regarding deep learning directed at lung diseases detection. However, their survey is lacking in the presentation of taxonomy and analysis of the trend of recent work. The objectives of this paper are to present a taxonomy of the state-of-the-art deep learning based lung disease detection systems, visualise the trends of recent work on the domain and identify the remaining issues and potential future directions in this domain. Ninety-eight articles published from 2016 to 2020 were considered in this survey. The taxonomy consists of seven attributes that are common in the surveyed articles: image types, features, data augmentation, types of deep learning algorithms, transfer learning, the ensemble of classifiers and types of lung diseases. The presented taxonomy could be used by other researchers to plan their research contributions and activities. The potential future direction suggested could further improve the efficiency and increase the number of deep learning aided lung disease detection applications.

show abstract

“…The ability of deep learning to identify high-level features is shown to produce better classification results [7]. Numerous works using deep learning for TB detection on chest x-rays can be found in [8][9][10][11][12][13][14][15][16][17][18][19]. 714 Some studies used ensemble techniques, whereby more than one classifier was selected, to make predictions.…”

Section: Introductionmentioning

confidence: 99%

Tuberculosis detection using deep learning and contrastenhanced canny edge detected X-Ray images

Hwa

Abdullah

Hijazi

et al. 2020

IJ-AI

View full text Add to dashboard Cite

Tuberculosis (TB) is a disease that causes death if not treated early. Ensemble deep learning can aid early TB detection. Previous work trained the ensemble classifiers on images with similar features only. An ensemble requires a diversity of errors to perform well, which is achieved using either different classification techniques or feature sets. This paper focuses on the latter, where TB detection using deep learning and contrast-enhanced canny edge detected (CEED-Canny) x-ray images is presented. The CEED-Canny was utilized to produce edge detected images of the lung x-ray. Two types of features were generated; the first was extracted from the Enhanced x-ray images, while the second from the Edge detected images. The proposed variation of features increased the diversity of errors of the base classifiers and improved the TB detection. The proposed ensemble method produced a comparable accuracy of 93.59%, sensitivity of 92.31% and specificity of 94.87% with previous work.

show abstract

Automated TB classification using ensemble of deep architectures

Cited by 41 publications

References 48 publications

Medical image analysis based on deep learning approach

Medical image analysis based on deep learning approach

A Survey of Deep Learning for Lung Disease Detection on Medical Images: State-of-the-Art, Taxonomy, Issues and Future Directions

Tuberculosis detection using deep learning and contrastenhanced canny edge detected X-Ray images

Contact Info

Product

Resources

About