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

Quality Assessment of Plain Chest X-Rays of the Expatriates in Jordan: Multi-Centres Study

Background

High-quality diagnostic imaging is critical to patient safety and effective healthcare. This study assesses the quality of plain chest X-rays (CXR) for expatriates in Jordan, with a focus on complying to American College of Radiology (ACR) and European Commission (EC) criteria at 12 healthcare facilities.

Methods

A retrospective cross-sectional evaluation of 1020 CXR radiographs from 2023 was performed. A quantitative classification approach was used to assess CXR for ACR and EC compliance, which included exposure, anatomical visibility, organ superimposition, and image annotation.

Results

Only 15% of CXRs passed all of the quality standards. While exposure and anatomical visualization demonstrated adequate compliance, organ superimposition and image annotation were much lower, at 23% and 29%, respectively, revealing gaps that may impact diagnostic accuracy and patient safety.

Conclusion

Significant discrepancies in CXR quality highlight the need for enhanced training, rigorous adherence to imaging protocols, and robust quality control to improve diagnosis accuracy and patient outcomes, especially in expatriate screening programs.

Active case finding to detect symptomatic and subclinical pulmonary tuberculosis disease: implementation of computer-aided detection for chest radiography in Viet Nam

Objective

In Viet Nam, tuberculosis (TB) prevalence surveys revealed that approximately 98% of individuals with pulmonary TB have TB-presumptive abnormalities on chest radiographs, while 32% have no TB symptoms. This prompted the adoption of the "Double X" strategy, which combines chest radiographs and computer-aided detection with GeneXpert testing to screen for and diagnose TB among vulnerable populations. The aim of this study was to describe demographic, clinical and radiographic characteristics of symptomatic and asymptomatic Double X participants and to assess multilabel radiographic abnormalities on chest radiographs, interpreted by computer-aided detection software, as a possible tool for detecting TB-presumptive abnormalities, particularly for subclinical TB.

Methods

Double X participants with TB-presumptive chest radiographs and/or TB symptoms and known risks were referred for confirmatory GeneXpert testing. The demographic and clinical characteristics of all Double X participants and the subset with confirmed TB were summarized. Univariate and multivariable logistic regression modelling was used to evaluate associations between participant characteristics and subclinical TB and between computer-aided detection multilabel radiographic abnormalities and TB.

Results

From 2020 to 2022, 96 631 participants received chest radiographs, with 67 881 (70.2%) reporting no TB symptoms. Among 1144 individuals with Xpert-confirmed TB, 51.0% were subclinical. Subclinical TB prevalence was higher in older age groups, non-smokers, those previously treated for TB and the northern region. Among 11 computer-aided detection multilabel radiographic abnormalities, fibrosis was associated with higher odds of subclinical TB.

Discussion

In Viet Nam, Double X community case finding detected pulmonary TB, including subclinical TB. Computer-aided detection software may have the potential to identify subclinical TB on chest radiographs by classifying multilabel radiographic abnormalities, but further research is needed.

Deep Learning-Based System Combining Chest X-Ray and Computerized Tomography Images for COVID-19 Diagnosis

Aims/Background: The coronavirus disease 2019 (COVID-19) pandemic has highlighted the need for accurate and efficient diagnostic methods. This study aims to improve COVID-19 detection by integrating chest X-ray (CXR) and computerized tomography (CT) images using deep learning techniques, further improving diagnostic accuracy by using a combined imaging approach. Methods: The study used two publicly accessible databases, COVID-19 Questionnaires for Understanding the Exposure (COVID-QU-Ex) and Integrated Clinical and Translational Cancer Foundation (iCTCF), containing CXR and CT images, respectively. The proposed system employed convolutional neural networks (CNNs) for classification, specifically EfficientNet and ResNet architectures. The data underwent preprocessing steps, including image resizing, Gaussian noise addition, and data augmentation. The dataset was divided into training, validation, and test sets. Gradient-weighted Class Activation Mapping (Grad-CAM) was used for model interpretability. Results: The EfficientNet-based models outperformed the ResNet-based models across all metrics. The highest accuracy achieved was 99.44% for CXR images and 99.81% for CT images with EfficientNetB5. The models also demonstrated high precision, recall, and F1 scores. For statistical significance, the p-values were less than 0.05, indicating that the results are significant. Conclusion: Integrating CXR and CT images using deep learning significantly improves the accuracy of COVID-19 diagnosis. The EfficientNet-based models, with their superior feature extraction capabilities, show better performance than ResNet models. Grad-CAM Visualizations provide insights into the model's decision-making process, potentially reducing diagnostic errors and accelerating diagnosis processes. This approach can improve patient care and support healthcare systems in managing the pandemic more effectively.

A Parkinson's Auxiliary Diagnosis Algorithm Based on a Hyperparameter Optimization Method of Deep Learning

Parkinson's disease is a common mental disease in the world, especially in the middle-aged and elderly groups. Today, clinical diagnosis is the main diagnostic method of Parkinson's disease, but the diagnosis results are not ideal, especially in the early stage of the disease. In this paper, a Parkinson's auxiliary diagnosis algorithm based on a hyperparameter optimization method of deep learning is proposed for the Parkinson's diagnosis. The diagnosis system uses ResNet50 to achieve feature extraction and Parkinson's classification, mainly including speech signal processing part, algorithm improvement part based on Artificial Bee Colony algorithm (ABC) and optimizing the hyperparameters of ResNet50 part. The improved algorithm is called Gbest Dimension Artificial Bee Colony algorithm (GDABC), proposing "Range pruning strategy" which aims at narrowing the scope of search and "Dimension adjustment strategy" which is to adjust gbest dimension by dimension. The accuracy of the diagnosis system in the verification set of Mobile Device Voice Recordings at King's College London (MDVR-CKL) dataset can reach more than 96%. Compared with current Parkinson's sound diagnosis methods and other optimization algorithms, our auxiliary diagnosis system shows better classification performance on the dataset within limited time and resources.

Deep Learning-Based Classification and Semantic Segmentation of Lung Tuberculosis Lesions in Chest X-ray Images

We present a deep learning (DL) network-based approach for detecting and semantically segmenting two specific types of tuberculosis (TB) lesions in chest X-ray (CXR) images. In the proposed method, we use a basic U-Net model and its enhanced versions to detect, classify, and segment TB lesions in CXR images. The model architectures used in this study are U-Net, Attention U-Net, U-Net++, Attention U-Net++, and pyramid spatial pooling (PSP) Attention U-Net++, which are optimized and compared based on the test results of each model to find the best parameters. Finally, we use four ensemble approaches which combine the top five models to further improve lesion classification and segmentation results. In the training stage, we use data augmentation and preprocessing methods to increase the number and strength of lesion features in CXR images, respectively. Our dataset consists of 110 training, 14 validation, and 98 test images. The experimental results show that the proposed ensemble model achieves a maximum mean intersection-over-union (MIoU) of 0.70, a mean precision rate of 0.88, a mean recall rate of 0.75, a mean F1-score of 0.81, and an accuracy of 1.0, which are all better than those of only using a single-network model. The proposed method can be used by clinicians as a diagnostic tool assisting in the examination of TB lesions in CXR images.

Observer Performance Evaluation of a Deep Learning Model for Multilabel Classification of Active Tuberculosis Lung Zone-Wise Manifestations

Background Chest X-rays (CXRs) are widely used for cost-effective screening of active pulmonary tuberculosis despite their limitations in sensitivity and specificity when interpreted by clinicians or radiologists. To address this issue, computer-aided detection (CAD) algorithms, particularly deep learning architectures based on convolution, have been developed to automate the analysis of radiography imaging. Deep learning algorithms have shown promise in accurately classifying lung abnormalities using chest X-ray images. In this study, we utilized the EfficientNet B4 model, which was pre-trained on ImageNet with 380x380 input dimensions, using its weights for transfer learning, and was modified with a series of components including global average pooling, batch normalization, dropout, and a classifier with 12 image-wise and 44 segment-wise lung zone evaluation classes using sigmoid activation. Objectives Assess the clinical usefulness of our previously created EfficientNet B4 model in identifying lung zone-specific abnormalities related to active tuberculosis through an observer performance test involving a skilled clinician operating in tuberculosis-specific environments. Methods The ground truth was established by a radiologist who examined all sample CXRs to identify lung zone-wise abnormalities. An expert clinician working in tuberculosis-specific settings independently reviewed the same CXR with blinded access to the ground truth. Simultaneously, the CXRs were classified using the EfficientNet B4 model. The clinician's assessments were then compared with the model's predictions, and the agreement between the two was measured using the kappa coefficient, evaluating the model's performance in classifying active tuberculosis manifestations across lung zones. Results The results show a strong agreement (Kappa ≥0.81) seen for lung zone-wise abnormalities of pneumothorax, mediastinal shift, emphysema, fibrosis, calcifications, pleural effusion, and cavity. Substantial agreement (Kappa = 0.61-0.80) for cavity, mediastinal shift, volume loss, and collapsed lungs. The Kappa score for lung zone-wise abnormalities is moderate (0.41-0.60) for 39% of cases. In image-wise agreement, the EfficientNet B4 model's performance ranges from moderate to almost perfect across categories, while in lung zone-wise agreement, it varies from fair to almost perfect. The results show strong agreement between the EfficientNet B4 model and the human reader in detecting lung zone-wise and image-wise manifestations. Conclusion The clinical utility of the EfficientNet B4 models to detect the abnormalities can aid clinicians in primary care settings for screening and triaging tuberculosis where resources are constrained or overburdened.

Weak Localization of Radiographic Manifestations in Pulmonary Tuberculosis from Chest X-ray: A Systematic Review

Pulmonary tuberculosis (PTB) is a bacterial infection that affects the lung. PTB remains one of the infectious diseases with the highest global mortalities. Chest radiography is a technique that is often employed in the diagnosis of PTB. Radiologists identify the severity and stage of PTB by inspecting radiographic features in the patient's chest X-ray (CXR). The most common radiographic features seen on CXRs include cavitation, consolidation, masses, pleural effusion, calcification, and nodules. Identifying these CXR features will help physicians in diagnosing a patient. However, identifying these radiographic features for intricate disorders is challenging, and the accuracy depends on the radiologist's experience and level of expertise. So, researchers have proposed deep learning (DL) techniques to detect and mark areas of tuberculosis infection in CXRs. DL models have been proposed in the literature because of their inherent capacity to detect diseases and segment the manifestation regions from medical images. However, fully supervised semantic segmentation requires several pixel-by-pixel labeled images. The annotation of such a large amount of data by trained physicians has some challenges. First, the annotation requires a significant amount of time. Second, the cost of hiring trained physicians is expensive. In addition, the subjectivity of medical data poses a difficulty in having standardized annotation. As a result, there is increasing interest in weak localization techniques. Therefore, in this review, we identify methods employed in the weakly supervised segmentation and localization of radiographic manifestations of pulmonary tuberculosis from chest X-rays. First, we identify the most commonly used public chest X-ray datasets for tuberculosis identification. Following that, we discuss the approaches for weakly localizing tuberculosis radiographic manifestations in chest X-rays. The weakly supervised localization of PTB can highlight the region of the chest X-ray image that contributed the most to the DL model's classification output and help pinpoint the diseased area. Finally, we discuss the limitations and challenges of weakly supervised techniques in localizing TB manifestations regions in chest X-ray images.

Estimating the Volume of Nodules and Masses on Serial Chest Radiography Using a Deep-Learning-Based Automatic Detection Algorithm: A Preliminary Study

BACKGROUND

The purpose of this study was to assess the volume of the pulmonary nodules and masses on serial chest X-rays (CXRs) from deep-learning-based automatic detection algorithm (DLAD)-based parameters.

METHODS

In a retrospective single-institutional study, 72 patients, who obtained serial CXRs (n = 147) for pulmonary nodules or masses with corresponding chest CT images as the reference standards, were included. A pre-trained DLAD based on a convolutional neural network was developed to detect and localize nodules using 13,710 radiographs and to calculate a localization map and the derived parameters (e.g., the area and mean probability value of pulmonary nodules) for each CXR, including serial follow-ups. For validation, reference 3D CT volumes were measured semi-automatically. Volume prediction models for pulmonary nodules were established through univariable or multivariable, and linear or non-linear regression analyses with the parameters. A polynomial regression analysis was performed as a method of a non-linear regression model.

RESULTS

Of the 147 CXRs and 208 nodules of 72 patients, the mean volume of nodules or masses was measured as 9.37 ± 11.69 cm³ (mean ± standard deviation). The area and CT volume demonstrated a linear correlation of moderate strength (i.e., R = 0.58, RMSE: 9449.9 mm³ m³ in a linear regression analysis). The area and mean probability values exhibited a strong linear correlation (R = 0.73). The volume prediction performance based on a multivariable regression model was best with a mean probability and unit-adjusted area (i.e.

, RMSE

7975.6 mm³, the smallest among the other variable parameters).

CONCLUSIONS

The prediction model with the area and the mean probability based on the DLAD showed a rather accurate quantitative estimation of pulmonary nodule or mass volume and the change in serial CXRs.

Sputum smears quality inspection using an ensemble feature extraction approach

The diagnosis of tuberculosis (TB) is extremely important. Sputum smear microscopy is thought to be the best method available in terms of accessibility and ease of use in resource-constrained countries. In this paper, research was conducted to evaluate the effectiveness of tuberculosis diagnosis by examining, among other things, the underlying causes of sputum smear quality for Ethiopian states such as Tigray, Amahira, and Oromia. However, because it is done manually, it has its limitations. This study proposes a model for sputum smear quality inspection using an ensemble feature extraction approach. The dataset used was recorded and labeled by experts in a regional lab in Bahir Dar, near Felege Hiwot Hospital after being collected from Gabi Hospital, Felege Hiwot Hospital, Adit Clinic and Gondar Hospital, as well as Kidanemihret Clinic in Gondar. We used a controlled environment to reduce environmental influences and eliminate variation. All the data was collected using a smartphone (the standard 15) with a jpg file extension and a pixel resolution of 1,728 × 3,840. Prior to feature extraction, bicubic resizing, and ROI extraction using thresholding was performed. In addition, sequential Gaussian and Gabor filters were used for noise reduction, augmentation, and CLAHE was used for enhancement. For feature extraction, GLCM from the gray label and CNN from the color image were both chosen. Ultimately, when CNN, SVM, and KNN classifiers were used to test both CNN and GLCM features, KNN outperformed them all with scores of 87, 93, and 94% for GLCM, CNN, and a hybrid of CNN and GLCM, respectively. CNN with GLCM outperformed other methods by 0.7 and 0.1% for GLCM and CNN feature extractors using the same classifier, respectively. In addition, the KNN classifier with the combination of CNN and GLCM as feature extractors performed better than existing methods by 1.48%.

Deep learning classification of active tuberculosis lung zones wise manifestations using chest X-rays: a multi label approach

Chest X-rays are the most economically viable diagnostic imaging test for active pulmonary tuberculosis screening despite the high sensitivity and low specificity when interpreted by clinicians or radiologists. Computer aided detection (CAD) algorithms, especially convolution based deep learning architecture, have been proposed to facilitate the automation of radiography imaging modalities. Deep learning algorithms have found success in classifying various abnormalities in lung using chest X-ray. We fine-tuned, validated and tested EfficientNetB4 architecture and utilized the transfer learning methodology for multilabel approach to detect lung zone wise and image wise manifestations of active pulmonary tuberculosis using chest X-ray. We used Area Under Receiver Operating Characteristic (AUC), sensitivity and specificity along with 95% confidence interval as model evaluation metrics. We also utilized the visualisation capabilities of convolutional neural networks (CNN), Gradient-weighted Class Activation Mapping (Grad-CAM) as post-hoc attention method to investigate the model and visualisation of Tuberculosis abnormalities and discuss them from radiological perspectives. EfficientNetB4 trained network achieved remarkable AUC, sensitivity and specificity of various pulmonary tuberculosis manifestations in intramural test set and external test set from different geographical region. The grad-CAM visualisations and their ability to localize the abnormalities can aid the clinicians at primary care settings for screening and triaging of tuberculosis where resources are constrained or overburdened.

Ensemble Technique Coupled with Deep Transfer Learning Framework for Automatic Detection of Tuberculosis from Chest X-ray Radiographs

Tuberculosis (TB) is an infectious disease affecting humans' lungs and is currently ranked the 13th leading cause of death globally. Due to advancements in technology and the availability of medical datasets, automatic analysis and classification of chest X-rays (CXRs) into TB and non-TB can be a reliable alternative for early TB screening. We propose an automatic TB detection system using advanced deep learning (DL) models. A substantial part of a CXR image is dark, with no relevant information for diagnosis and potentially confusing DL models. In this work, the U-Net model extracts the region of interest from CXRs and the segmented images are fed to the DL models for feature extraction. Eight different convolutional neural networks (CNN) models are employed in our experiments, and their classification performance is compared based on three publicly available CXR datasets. The U-Net model achieves segmentation accuracy of 98.58%, intersection over union (IoU) of 93.10, and a Dice coefficient score of 96.50. Our proposed stacked ensemble algorithm performed better by achieving accuracy, sensitivity, and specificity values of 98.38%, 98.89%, and 98.70%, respectively. Experimental results confirm that segmented lung CXR images with ensemble learning produce a better result than un-segmented lung CXR images.

An efficient deep learning-based framework for tuberculosis detection using chest X-ray images

Early diagnosis of tuberculosis (TB) is an essential and challenging task to prevent disease, decrease mortality risk, and stop transmission to other people. The chest X-ray (CXR) is the top choice for lung disease screening in clinics because it is cost-effective and easily accessible in most countries. However, manual screening of CXR images is a heavy burden for radiologists, resulting in a high rate of inter-observer variances. Hence, proposing a cost-effective and accurate computer aided diagnosis (CAD) system for TB diagnosis is challenging for researchers. In this research, we proposed an efficient and straightforward deep learning network called TBXNet, which can accurately classify a large number of TB CXR images. The network is based on five dual convolutions blocks with varying filter sizes of 32, 64, 128, 256 and 512, respectively. The dual convolution blocks are fused with a pre-trained layer in the fusion layer of the network. In addition, the pre-trained layer is utilized for transferring pre-trained knowledge into the fusion layer. The proposed TBXNet has achieved an accuracy of 98.98%, and 99.17% on Dataset A and Dataset B, respectively. Furthermore, the generalizability of the proposed work is validated against Dataset C, which is based on normal, tuberculous, pneumonia, and COVID-19 CXR images. The TBXNet has obtained the highest results in Precision (95.67%), Recall (95.10%), F1-score (95.38%), and Accuracy (95.10%), which is comparatively better than all other state-of-the-art methods.

A multichannel EfficientNet deep learning-based stacking ensemble approach for lung disease detection using chest X-ray images

This paper proposes a multichannel deep learning approach for lung disease detection using chest X-rays. The multichannel models used in this work are EfficientNetB0, EfficientNetB1, and EfficientNetB2 pretrained models. The features from EfficientNet models are fused together. Next, the fused features are passed into more than one non-linear fully connected layer. Finally, the features passed into a stacked ensemble learning classifier for lung disease detection. The stacked ensemble learning classifier contains random forest and SVM in the first stage and logistic regression in the second stage for lung disease detection. The performance of the proposed method is studied in detail for more than one lung disease such as pneumonia, Tuberculosis (TB), and COVID-19. The performances of the proposed method for lung disease detection using chest X-rays compared with similar methods with the aim to show that the method is robust and has the capability to achieve better performances. In all the experiments on lung disease, the proposed method showed better performance and outperformed similar lung disease existing methods. This indicates that the proposed method is robust and generalizable on unseen chest X-rays data samples. To ensure that the features learnt by the proposed method is optimal, t-SNE feature visualization was shown on all three lung disease models. Overall, the proposed method has shown 98% detection accuracy for pediatric pneumonia lung disease, 99% detection accuracy for TB lung disease, and 98% detection accuracy for COVID-19 lung disease. The proposed method can be used as a tool for point-of-care diagnosis by healthcare radiologists.Journal instruction requires a city for affiliations; however, this is missing in affiliation 3. Please verify if the provided city is correct and amend if necessary.correct.

Prediction and Diagnosis of Respiratory Disease by Combining Convolutional Neural Network and Bi-directional Long Short-Term Memory Methods

Objective

Based on the respiratory disease big data platform in southern Xinjiang, we established a model that predicted and diagnosed chronic obstructive pulmonary disease, bronchiectasis, pulmonary embolism and pulmonary tuberculosis, and provided assistance for primary physicians.

Methods

The method combined convolutional neural network (CNN) and long-short-term memory network (LSTM) for prediction and diagnosis of respiratory diseases. We collected the medical records of inpatients in the respiratory department, including: chief complaint, history of present illness, and chest computed tomography. Pre-processing of clinical records with "jieba" word segmentation module, and the Bidirectional Encoder Representation from Transformers (BERT) model was used to perform word vectorization on the text. The partial and total information of the fused feature set was encoded by convolutional layers, while LSTM layers decoded the encoded information.

Results

The precisions of traditional machine-learning, deep-learning methods and our proposed method were 0.6, 0.81, 0.89, and F1 scores were 0.6, 0.81, 0.88, respectively.

Conclusion

Compared with traditional machine learning and deep-learning methods that our proposed method had a significantly higher performance, and provided precise identification of respiratory disease.

Enhanced lung image segmentation using deep learning

With the advances in technology, assistive medical systems are emerging with rapid growth and helping healthcare professionals. The proactive diagnosis of diseases with artificial intelligence (AI) and its aligned technologies has been an exciting research area in the last decade. Doctors usually detect tuberculosis (TB) by checking the lungs' X-rays. Classification using deep learning algorithms is successfully able to achieve accuracy almost similar to a doctor in detecting TB. It is found that the probability of detecting TB increases if classification algorithms are implemented on segmented lungs instead of the whole X-ray. The paper's novelty lies in detailed analysis and discussion of U-Net +  + results and implementation of U-Net +  + in lung segmentation using X-ray. A thorough comparison of U-Net +  + with three other benchmark segmentation architectures and segmentation in diagnosing TB or other pulmonary lung diseases is also made in this paper. To the best of our knowledge, no prior research tried to implement U-Net +  + for lung segmentation. Most of the papers did not even use segmentation before classification, which causes data leakage. Very few used segmentations before classification, but they only used U-Net, which U-Net +  + can easily replace because accuracy and mean_iou of U-Net +  + are greater than U-Net accuracy and mean_iou , discussed in results, which can minimize data leakage. The authors achieved more than 98% lung segmentation accuracy and mean_iou 0.95 using U-Net +  + , and the efficacy of such comparative analysis is validated.