TX-CNN: Detecting tuberculosis in chest X-ray images using convolutional neural network

Chang, Liu; Cao, Yu; Alcântara, Maria Regina; Liu, Benyuan; Brunette, Maria; Peinado, Jesús; Curioso, Walter H.

doi:10.1109/icip.2017.8296695

Cited by 143 publications

(96 citation statements)

References 13 publications

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“…A 12-classed chest X-ray image dataset was used, and an accuracy score of 86% was reported. Liu et al [12] proposed a deep learning-based approach for tuberculosis detection. Authors developed a new CNN architecture for detection of the tuberculosis based on chest X-ray images.…”

Section: Introductionmentioning

confidence: 99%

The investigation of multiresolution approaches for chest X-ray image based COVID-19 detection

Ismael

Şengür

2020

Health Inf Sci Syst

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COVID-19 is a novel virus, which has a fast spreading rate, and now it is seen all around the world. The case and death numbers are increasing day by day. Some tests have been used to determine the COVID-19. Chest X-ray and chest computerized tomography (CT) are two important imaging tools for determination and monitoring of COVID-19. And new methods have been searching for determination of the COVID-19. In this paper, the investigation of various multiresolution approaches in detection of COVID-19 is carried out. Chest X-ray images are used as input to the proposed approach. As recent trend in machine learning shifts toward the deep learning, we would like to show that the traditional methods such as multiresolution approaches are still effective. To this end, the well-known multiresolution approaches namely Wavelet, Shearlet and Contourlet transforms are used to decompose the chest X-ray images and the entropy and the normalized energy approaches are employed for feature extraction from the decomposed chest X-ray images. Entropy and energy features are generally accompanied with the multiresolution approaches in texture recognition applications. The extreme learning machines (ELM) classifier is considered in the classification stage of the proposed study. A dataset containing 361 different COVID-19 chest X-ray images and 200 normal (healthy) chest X-ray images are used in the experimental works. The performance evaluation is carried out by employing various metric namely accuracy, sensitivity, specificity and precision. As deep learning is mentioned, a comparison between proposed multiresolution approaches and deep learning approaches is also carried out. To this end, deep feature extraction and fine-tuning of pretrained convolutional neural networks (CNNs) are considered. For deep feature extraction, pretrained, ResNet50 model is employed. For classification of the deep features, the Support Vector Machines (SVM) classifier is used. The ResNet50 model is also used in the fine-tuning. The experimental works show that multiresolution approaches produced better performance than the deep learning approaches. Especially, Shearlet transform outperformed at all. 99.29% accuracy score is obtained by using Shearlet transform.

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Section: Introductionmentioning

confidence: 99%

The investigation of multiresolution approaches for chest X-ray image based COVID-19 detection

Ismael

Şengür

2020

Health Inf Sci Syst

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“…Hence, it is important to develop alternative solutions, based on reliable automated analysis and interpretation of MODS cultures. Liu et al (2017) proposed a novel method using CNN to deal with unbalanced, less-category X-ray images. Their method improves the accuracy for classifying multiple TB manifestations by a large margin.…”

Section: Related Workmentioning

confidence: 99%

Tuberculosis Diagnostics and Localization in Chest X-Rays via Deep Learning Models

Guo

Passi

Jain

2020

Front. Artif. Intell.

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For decades, tuberculosis (TB), a potentially serious infectious lung disease, continues to be a leading cause of worldwide death. Proven to be conveniently efficient and cost-effective, chest X-ray (CXR) has become the preliminary medical imaging tool for detecting TB. Arguably, the quality of TB diagnosis will improve vastly with automated CXRs for TB detection and the localization of suspected areas, which may manifest TB. The current line of research aims to develop an efficient computer-aided detection system that will support doctors (and radiologists) to become well-informed when making TB diagnosis from patients' CXRs. Here, an integrated process to improve TB diagnostics via convolutional neural networks (CNNs) and localization in CXRs via deep-learning models is proposed. Three key steps in the TB diagnostics process include (a) modifying CNN model structures, (b) model fine-tuning via artificial bee colony algorithm, and (c) the implementation of linear average-based ensemble method. Comparisons of the overall performance are made across all three steps among the experimented deep CNN models on two publicly available CXR datasets, namely, the Shenzhen Hospital CXR dataset and the National Institutes of Health CXR dataset. Validated performance includes detecting CXR abnormalities and differentiating among seven TB-related manifestations (consolidation, effusion, fibrosis, infiltration, mass, nodule, and pleural thickening). Importantly, class activation mapping is employed to inform a visual interpretation of the diagnostic result by localizing the detected lung abnormality manifestation on CXR. Compared to the state-of-the-art, the resulting approach showcases an outstanding performance both in the lung abnormality detection and the specific TB-related manifestation diagnosis vis-à-vis the localization in CXRs.

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“…The usage and importance of deep learning applications within the radiological workflow is increasing. A majority of scientific research is based on the aim of the automatic detection of diseases on CT, MR or X-ray images via deep learning algorithms [1][2][3]. To implement those kinds of algorithms researchers have to rely on valid information including not only the image data itself but also important metadata that are needed for the training and decision process [4].…”

Section: Introductionmentioning

confidence: 99%

Differentiation Between Anteroposterior and Posteroanterior Chest X-Ray View Position With Convolutional Neural Networks

Hosch

Kroll

Nensa

et al. 2020

Rofo

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Purpose Detection and validation of the chest X-ray view position with use of convolutional neural networks to improve meta-information for data cleaning within a hospital data infrastructure. Material and Methods Within this paper we developed a convolutional neural network which automatically detects the anteroposterior and posteroanterior view position of a chest radiograph. We trained two different network architectures (VGG variant and ResNet-34) with data published by the RSNA (26 684 radiographs, class distribution 46 % AP, 54 % PA) and validated these on a self-compiled dataset with data from the University Hospital Essen (4507, radiographs, class distribution 55 % PA, 45 % AP) labeled by a human reader. For visualization and better understanding of the network predictions, a Grad-CAM was generated for each network decision. The network results were evaluated based on the accuracy, the area under the curve (AUC), and the F1-score against the human reader labels. Also a final performance comparison between model predictions and DICOM labels was performed. Results The ensemble models reached accuracy and F1-scores greater than 95 %. The AUC reaches more than 0.99 for the ensemble models. The Grad-CAMs provide insight as to which anatomical structures contributed to a decision by the networks which are comparable with the ones a radiologist would use. Furthermore, the trained models were able to generalize over mislabeled examples, which was found by comparing the human reader labels to the predicted labels as well as the DICOM labels. Conclusion The results show that certain incorrectly entered meta-information of radiological images can be effectively corrected by deep learning in order to increase data quality in clinical application as well as in research. Key Points: Citation Format

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TX-CNN: Detecting tuberculosis in chest X-ray images using convolutional neural network

Cited by 143 publications

References 13 publications

The investigation of multiresolution approaches for chest X-ray image based COVID-19 detection

The investigation of multiresolution approaches for chest X-ray image based COVID-19 detection

Tuberculosis Diagnostics and Localization in Chest X-Rays via Deep Learning Models

Differentiation Between Anteroposterior and Posteroanterior Chest X-Ray View Position With Convolutional Neural Networks

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