Deep learning-based automatic detection of tuberculosis disease in chest X-ray images

Showkatian, Eman; Salehi, Mohammad; Ghaffari, Hamed; Reiazi, Reza; Sadighi, Nahid

doi:10.5114/pjr.2022.113435

Cited by 47 publications

(18 citation statements)

References 20 publications

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“…Results show the effectiveness of the attention mechanism with an ACC of 92.73%, a PRE of 92.73%, an F1-score of 92.82% and an AUC of 97.71%. Eman et al [118] introduced a DCNN model (ConvNet) trained from scratch to automatically detect TB on CXR images. Furthermore, a transfer learning techniques with five different pre-trained models (VGG-16, VGG-19, Inception-V3, ResNet-50 and, Xception) was used to evaluate the performance of each model with the proposed technique.…”

Section: Tuberculosis Detectionmentioning

confidence: 99%

A Review of Recent Advances in Deep Learning Models for Chest Disease Detection Using Radiography

Nasser

Akhloufi

2023

Diagnostics

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Chest X-ray radiography (CXR) is among the most frequently used medical imaging modalities. It has a preeminent value in the detection of multiple life-threatening diseases. Radiologists can visually inspect CXR images for the presence of diseases. Most thoracic diseases have very similar patterns, which makes diagnosis prone to human error and leads to misdiagnosis. Computer-aided detection (CAD) of lung diseases in CXR images is among the popular topics in medical imaging research. Machine learning (ML) and deep learning (DL) provided techniques to make this task more efficient and faster. Numerous experiments in the diagnosis of various diseases proved the potential of these techniques. In comparison to previous reviews our study describes in detail several publicly available CXR datasets for different diseases. It presents an overview of recent deep learning models using CXR images to detect chest diseases such as VGG, ResNet, DenseNet, Inception, EfficientNet, RetinaNet, and ensemble learning methods that combine multiple models. It summarizes the techniques used for CXR image preprocessing (enhancement, segmentation, bone suppression, and data-augmentation) to improve image quality and address data imbalance issues, as well as the use of DL models to speed-up the diagnosis process. This review also discusses the challenges present in the published literature and highlights the importance of interpretability and explainability to better understand the DL models’ detections. In addition, it outlines a direction for researchers to help develop more effective models for early and automatic detection of chest diseases.

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Section: Tuberculosis Detectionmentioning

confidence: 99%

A Review of Recent Advances in Deep Learning Models for Chest Disease Detection Using Radiography

Nasser

Akhloufi

2023

Diagnostics

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“…Deep convolutional neural networks (CNNs), as a deep learning method, outperform alternative methods, i.e. classic machine learning methods, in a wide range of applications [17][18][19][20], including medical image segmentation [21,22]. Therefore, deep CNNs can be considered as the state-of-the art computer-assisted method for segmentation.…”

Section: E32mentioning

confidence: 99%

Fully automated clinical target volume segmentation for glioblastoma radiotherapy using a deep convolutional neural network

Sadeghi¹,

Farzin²,

Gholami³

2023

Pol J Radiol

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Purpose Target volume delineation is a crucial step prior to radiotherapy planning in radiotherapy for glioblastoma. This step is performed manually, which is time-consuming and prone to intra- and inter-rater variabilities. Therefore, the purpose of this study is to evaluate a deep convolutional neural network (CNN) model for automatic segmentation of clinical target volume (CTV) in glioblastoma patients. Material and methods In this study, the modified Segmentation-Net (SegNet) model with deep supervision and residual-based skip connection mechanism was trained on 259 glioblastoma patients from the Multimodal Brain Tumour Image Segmentation Benchmark (BraTS) 2019 Challenge dataset for segmentation of gross tumour volume (GTV). Then, the pre-trained CNN model was fine-tuned with an independent clinical dataset ( n = 37) to perform the CTV segmentation. In the process of fine-tuning, to generate a CT segmentation mask, both CT and MRI scans were simultaneously used as input data. The performance of the CNN model in terms of segmentation accuracy was evaluated on an independent clinical test dataset ( n = 15) using the Dice Similarity Coefficient (DSC) and Hausdorff distance. The impact of auto-segmented CTV definition on dosimetry was also analysed. Results The proposed model achieved the segmentation results with a DSC of 89.60 ± 3.56% and Hausdorff distance of 1.49 ± 0.65 mm. A statistically significant difference was found for the Dmin and Dmax of the CTV between manually and automatically planned doses. Conclusions The results of our study suggest that our CNN-based auto-contouring system can be used for segmentation of CTVs to facilitate the brain tumour radiotherapy workflow.

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“…In previous studies, deep learning, such as convolutional neural networks have achieved relatively good results in the classification and recognition of tuberculosis medical images. Eman Showkatian et al [8] proposed a CNN architecture (ConvNet), which achieved 88.0% accuracy, 87.0% sensitivity, 87.0% F1 score, 87.0% Precision and 87.0% AUC on the tuberculosis classification data sets in Shenzhen and Montgomery, China. Qingchen, Zhang et al [9]achieved an accuracy of 87.71% on the Montgomery tuberculosis classification dataset by changing the global average pooling of the network model to an adaptive dropout layer based on the residual network ResNet50.…”

Section: Related Workmentioning

confidence: 99%

Convolution Neural Network With Coordinate Attention for the Automatic Detection of Pulmonary Tuberculosis Images on Chest X-Rays

Yuan

2022

IEEE Access

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Tuberculosis is a chronic respiratory infectious disease that seriously endangers human health. Diagnosis of pulmonary tuberculosis usually depend on the analysis of chest X-rays by radiologists. However, there is a certain misdiagnosis rate with time consuming. Therefore, the purpose of this study is to propose a low-cost and automatic detection method of pulmonary tuberculosis images on chest X-rays to help primary radiologists. A pulmonary tuberculosis classification algorithm based on convolution neural network is proposed, which uses deep learning to classify chest X-ray images. Our method introduces coordinate attention mechanism into convolutional neural network (VGG16), so that the algorithm can capture not only cross-channel information, but also direction sensing and position sensing information, in order to better identify and classify pulmonary tuberculosis images. During the training process, we use the method of transfer learning and freeze network to make the model fit faster. The performance of our method is evaluated on the public dataset of tuberculosis classification of Shenzhen Third Hospital, China. We take the average data through 5-fold cross validation: accuracy= 92.73%, AUC= 97.71%, precision= 92.73%, recall= 92.83%, F1 score = 92.82%. Compared with the existing end-to-end method based on convolutional neural network (CNN), our method is superior to ConvNet, FPN + Faster RCNN and other methods. The comparison results with other methods show that our method has better accuracy, which can help radiologists make auxiliary diagnosis.

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Deep learning-based automatic detection of tuberculosis disease in chest X-ray images

Cited by 47 publications

References 20 publications

A Review of Recent Advances in Deep Learning Models for Chest Disease Detection Using Radiography

A Review of Recent Advances in Deep Learning Models for Chest Disease Detection Using Radiography

Fully automated clinical target volume segmentation for glioblastoma radiotherapy using a deep convolutional neural network

Convolution Neural Network With Coordinate Attention for the Automatic Detection of Pulmonary Tuberculosis Images on Chest X-Rays

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