A Robust Tuberculosis Diagnosis Using Chest X-Rays Based on a Hybrid Vision Transformer and Principal Component Analysis

Tuberculosis(TBC) remains a major global health issue, with millions of new cases reported annually. Early and accurate diagnosis is essential, but manual interpretation of chest X-ray(CXR) images is limited by subjectivity and resource constrains. This study applies the MobileNetV2 architecture using transfer learning to classify tuberculosis from CXR images. The publicly available Tuberculosis Chest X-ray dataset containing 4200 images was divided into training (70%), validation (15%), and testing (15%). The pretrained MobileNetV2 model on ImageNet was used as the base network, with additional classification layers and training through the Adam optimizer and early stopping. The model achieved a validation accuracy above 99.84% after the second epoch maintained stable performance. Once the test set, model reached 99.84% accuracy, with precision 99.53% and recall 99.90% for the tuberculosis class. The result demonstrate that the transfer learning with MobileNetV2 provides a fast, efficient, and highly accurate method for tuberculosis detection. This model show potential for integration into Computer-Aided Diagnosis (CAD) system in low resource clinical settings.

Tuberculosis (TB) is one of the most common diseases in developing countries such as Indonesia. TB disease oftenly attacks the human lungs due to its high oxygen pressure. The basic characteristics of TB-infected lungs are in the form of wounds, which change its texture and shape. The common detection method is by comparing the X-ray thoracic image with the tuberculin skin test (TST) and fast acid staining methods. This diagnosis method is often constrained by the availability of radiologists in the health facilities. To overcome such situation, a computer-aided diagnosis (CAD) tool is required to assist the doctor read the X-ray images easily. In this paper, a complementary detection method is proposed by using a chest X-ray (CXR) image to assist the diagnosis process. In current method, the image is segmented to reduce unnecessary parts prior to preprocessing step. We use a texture-based feature approach for detection purpose because most of the common examination methods for abnormality detection on CXRs is by analyzing the change of textural (content) CXR. In our texture approach, we use texture-based statistical properties on the histogram’s intensity. Some features that are used in this approach including Mean, Standard Deviation, Smoothness, Entropy, Root Mean Square (RMS), Variance, Kurtosis, Skewness, and Inverse Difference Moment (IDM). As a result of simulation, it indicates that CXR images can be well segmented to localize the focus area, allowing to the features can be extracted from the CXR image for further detection process. The obtained accuracy is about 93.94% and 92.86%, respectively for Shenzhen-Chest X-ray dataset and Montgomery County-Chest X-ray dataset.

PurposeTuberculosis (TB) is a severe bacterial infectious lung disease. Millions of people die or experience severe health complications due to TB each year. Accurate, automated, and effective detection of TB is key to curing and preventing person-to-person transmission. In this regard, deep learning (DL) and machine learning (ML) techniques applied to chest X-ray (CXR) images have proved effective. Here, we present our DL- and ML-based approach for TB detection using CXR images.Material and methodsWe implemented convolutional neural network (CNN)-based pre-trained DL models, such as DenseNet121, DenseNet169, DenseNet201, ResNet152, and VGG19, as feature extractors. ML models, including support vector machine (SVM), XGBoost, logistic regression, and a DL-based custom model, were used as classifiers. A total of 2,391 CXR images from three publicly available datasets were considered.ResultsWe found three models achieving the highest value in different evaluation metrics: accuracy (99.91%) with ResNet152 and SVM; recall (99.22%), precision (99.23%), and F1-score (99.22%) with DenseNet169 and the custom classifier; and area under the curve (99.99%) with DenseNet201 and the custom classifier. Among these models, we propose DenseNet169 with the custom classifier as the best performer for potential clinical application, as it reflected a high and well-balanced performance across all evaluation metrics.ConclusionsThis study evaluated pre-trained CNN-based DL and ML models on pre-processed CXR images for the detection of TB. The DenseNet169 model with a custom classifier stands out with its high and well-balanced performance, offering a significant contribution to the effective and automated detection of TB.

Tuberculosis (TB) is an infectious disease which becomes a significant health problem worldwide. Many people have been affected by this disease owing to deficiency of treatment and late or inaccuracy of diagnosis. Therefore, accurate and early diagnosis is the very major solution to checking and preventing the disease. A chest x-ray is a main diagnostic tool used to diagnose tuberculosis. This diagnostic method is limited by the availability of radiologists and the experience and skills of radiologists in reading x-rays. To overcome such a challenge, a computer-aided diagnosis (CAD) system is supposed for the radiologist to interpret chest x-ray images easily. In this study, a CAD system based upon transfer learning is developed for TB detection using Montgomery Country chest x-ray images. We used the VGG16, VGG19, DenseNet121, MobileNet, and InceptionV3 pre-trained CNN models to extract features automatically and used the Support Vector Machine (SVM) classifier to the detection of tuberculosis. Furthermore, data augmentation techniques were applied to boost the performance results. The proposed method performed the highest accuracy of 98.9% and area under the curve (AUC) of 1.00, respectively, with the DenseNet121 on augmented images.

Pneumonia is a respiratory infection caused by bacteria, virus or fungi. This infection affects the lungs by filling the air sacs(alveoli) with fluid or pus. Chest X-ray(CXR) imaging is the preferred diagnostic examination to locate the inflammation in the lungs. Examining the chest X-rays with high diagnostic accuracy is very difficult for the medical practitioners. To counteract this, computer-aided diagnosis system is used in this study for classifying the Chest X-ray (CXR) images into pneumonia and normal lungs. This research uses transfer learning based stacked ensemble CNN snapshots for the pneumonia prediction from the chest X-ray images. The DenseNet-121 architecture is used as the base learner. To reduce the computational time, the model is trained once to yield several CNN snapshots. The created snapshots give various details regarding the features taken from the CXR. The decision scores of multiple snapshots are combined using the stacking ensemble technique through a metalearner(Random Forest). The proposed ensemble model obtains an accuracy score of 98.36% on the publicly available Kermany dataset.

The coronavirus disease 2019 (COVID-19) led to a dramatic increase in the number of cases of patients with pneumonia worldwide. In this study, we aimed to develop an AI-assisted multistrategy image enhancement technique for chest X-ray (CXR) images to improve the accuracy of COVID-19 classification. Our new classification strategy consisted of 3 parts. First, the improved U-Net model with a variational encoder segmented the lung region in the CXR images processed by histogram equalization. Second, the residual net (ResNet) model with multidilated-rate convolution layers was used to suppress the bone signals in the 217 lung-only CXR images. A total of 80% of the available data were allocated for training and validation. The other 20% of the remaining data were used for testing. The enhanced CXR images containing only soft tissue information were obtained. Third, the neural network model with a residual cascade was used for the super-resolution reconstruction of low-resolution bone-suppressed CXR images. The training and testing data consisted of 1,200 and 100 CXR images, respectively. To evaluate the new strategy, improved visual geometry group (VGG)-16 and ResNet-18 models were used for the COVID-19 classification task of 2,767 CXR images. The accuracy of the multistrategy enhanced CXR images was verified through comparative experiments with various enhancement images. In terms of quantitative verification, 8-fold cross-validation was performed on the bone suppression model. In terms of evaluating the COVID-19 classification, the CXR images obtained by the improved method were used to train 2 classification models. Compared with other methods, the CXR images obtained based on the proposed model had better performance in the metrics of peak signal-to-noise ratio and root mean square error. The super-resolution CXR images of bone suppression obtained based on the neural network model were also anatomically close to the real CXR images. Compared with the initial CXR images, the classification accuracy rates of the internal and external testing data on the VGG-16 model increased by 5.09% and 12.81%, respectively, while the values increased by 3.51% and 18.20%, respectively, for the ResNet-18 model. The numerical results were better than those of the single-enhancement, double-enhancement, and no-enhancement CXR images. The multistrategy enhanced CXR images can help to classify COVID-19 more accurately than the other existing methods.

The world is witnessing an unprecedented catastrophe as a result of the COVID-19 (Coronavirus Disease) epidemic, which has spread to approximately 216 nations and territories throughout the globe. A COVID-19 infection may progress to pneumonia, which can be diagnosed by CXR (Chest X-Ray) examination and should be treated as soon as possible after diagnosis. This work is intended to examine the use of Artificial Intelligence (AI) in speedy & precise diagnosis of COVID-19 pneumonia utilizing digital CXR pictures, as well as to construct a robust computer-aided application for automated classification of COVID-19 pneumonia from other pneumonia as well as normal images. In this research, we use a standard machine learning (ML) technique that is effective. The SVM (Support Vector Machine) classification technique was used in the development of the model. The purpose of this research has been to determine the role of machine learning, image processing, image segmentation, and feature extraction in fast or accurate identification of COVID19 chest X-ray or CT images. We assessed the performance of ML techniques on chest X-ray pictures as well as CT scans to COVID-19 diagnosis in this paper. The model's performance was assessed using relevant classification measures, such as accuracy, precision, recall, & F1 score, among others. The model is capable of identifying COVID-19 patients from CXRs with training accuracy of 100 percent. We believe this high-accuracy & reasonably fast Computer-Aided Diagnosis (CAD) technique might be extremely beneficial in the containment of the pandemic.

To investigate the value of computer-aided diagnosis (CAD) system in assessing thyroid nodules concurrent with Hashimoto's thyroiditis (HT). Totally 148 patients with 193 thyroid nodules were enrolled. A radiologist assessed the nodules using a thyroid ultrasound CAD system. Additionally, the nodules were evaluated by one experienced radiologist alone, and one training radiologist without and with CAD assistance. The diagnostic performance was compared between the CAD system and the experienced radiologist, and the training radiologist without and with CAD assistance. The CAD system demonstrated a similar sensitivity to that of the experienced radiologist in diagnosing thyroid cancers (89.8% versus 92.4%, P > .05). The specificity and accuracy of the CAD system were lower than that of the experienced radiologist in assessing the nodules with diffusedly altered glands (specificity, 60.0% versus 81.7%, P=.007; accuracy, 77.5% versus 88.1%, P=.011). With CAD assistance, the training radiologist had improved sensitivity and accuracy that increased to 87.9% and 86.8% in classifying nodules with sonographically evident HT (both P=.012). The CAD system has comparable sensitivity, but lower specificity compared with the experienced radiologist in diagnosing thyroid malignancies concurrent with HT. For a radiologist with less experience, the CAD system can help improve the diagnostic performance by increasing sensitivity and accuracy in assessing thyroid nodules with diffusely altered parenchyma.

The chest digital tomosynthesis(CDT) is recently developed medical device that has several advantage for diagnosing lung disease. For example, CDT provides depth information with relatively low radiation dose compared to computed tomography (CT). However, a major problem with CDT is the image artifacts associated with data incompleteness resulting from limited angle data acquisition in CDT geometry. For this reason, the sensitivity of lung disease was not clear compared to CT. In this study, to improve sensitivity of lung disease detection in CDT, we developed computer aided diagnosis (CAD) systems based on machine learning. For design CAD systems, we used 100 cases of lung nodules cropped images and 100 cases of normal lesion cropped images acquired by lung man phantoms and proto type CDT. We used machine learning techniques based on support vector machine and Gabor filter. The Gabor filter was used for extracting characteristics of lung nodules and we compared performance of feature extraction of Gabor filter with various scale and orientation parameters. We used 3, 4, 5 scales and 4, 6, 8 orientations. After extracting features, support vector machine (SVM) was used for classifying feature of lesions. The linear, polynomial and Gaussian kernels of SVM were compared to decide the best SVM conditions for CDT reconstruction images. The results of CAD system with machine learning showed the capability of automatically lung lesion detection. Furthermore detection performance was the best when Gabor filter with 5 scale and 8 orientation and SVM with Gaussian kernel were used. In conclusion, our suggested CAD system showed improving sensitivity of lung lesion detection in CDT and decide Gabor filter and SVM conditions to achieve higher detection performance of our developed CAD system for CDT.

Computer-Aided Diagnosis (CAD) systems provide a second opinion to health professionals about the possible existence of an anomaly. Evaluation of CAD systems is a challenge and most of the traditional metrics requires the constant participation of experts. This paper presents an approach for evaluating CAD systems using concepts of Content-Based Image Retrieval and graphical oracles. After implementing feature descriptors and selecting three similarity functions, two metrics are proposed to measure the efficiency of CAD systems. A case study was conducted considering three simulated CAD systems to detect masses and calcifications in mammographic images. The results indicated that the our approach is as robust as traditional metrics with respect to performance evaluation. However, our approach is more flexible than traditional metrics because evaluators can choose the more adequate features to assess a particular CAD system.

Coronavirus (COVID-19) was first observed in Wuhan, China, and quickly propagated worldwide. It is considered the supreme crisis of the present era and one of the most crucial hazards threatening worldwide health. Therefore, the early detection of COVID-19 is essential. The common way to detect COVID-19 is the reverse transcription-polymerase chain reaction (RT-PCR) test, although it has several drawbacks. Computed tomography (CT) scans can enable the early detection of suspected patients, however, the overlap between patterns of COVID-19 and other types of pneumonia makes it difficult for radiologists to diagnose COVID-19 accurately. On the other hand, deep learning (DL) techniques and especially the convolutional neural network (CNN) can classify COVID-19 and non-COVID-19 cases. In addition, DL techniques that use CT images can deliver an accurate diagnosis faster than the RT-PCR test, which consequently saves time for disease control and provides an efficient computer-aided diagnosis (CAD) system. The shortage of publicly available datasets of CT images, makes the CAD system’s design a challenging task. The CAD systems in the literature are based on either individual CNN or two-fused CNNs; one used for segmentation and the other for classification and diagnosis. In this article, a novel CAD system is proposed for diagnosing COVID-19 based on the fusion of multiple CNNs. First, an end-to-end classification is performed. Afterward, the deep features are extracted from each network individually and classified using a support vector machine (SVM) classifier. Next, principal component analysis is applied to each deep feature set, extracted from each network. Such feature sets are then used to train an SVM classifier individually. Afterward, a selected number of principal components from each deep feature set are fused and compared with the fusion of the deep features extracted from each CNN. The results show that the proposed system is effective and capable of detecting COVID-19 and distinguishing it from non-COVID-19 cases with an accuracy of 94.7%, AUC of 0.98 (98%), sensitivity 95.6%, and specificity of 93.7%. Moreover, the results show that the system is efficient, as fusing a selected number of principal components has reduced the computational cost of the final model by almost 32%.

Tuberculosis (TB) continues to be a major cause of death from infectious diseases globally. TB is treatable with antibiotics, but it is often misdiagnosed or left untreated, particularly in rural and resource-constrained regions. While chest X-rays are a key tool in TB diagnosis, their effectiveness is hindered by the variability in radiological presentations and the lack of trained radiologists in high-prevalence areas. Deep learning-based imaging techniques offer a promising approach to computer-aided diagnosis for TB, enabling precise and timely detection while alleviating the burden on healthcare professionals. This study aims to enhance TB detection in chest X-ray images by developing deep learning models. We have observed upper and lower lobe consolidation, pleural effusion, calcification, cavity formation and military nodules. A proposed preprocessing technique has been also introduced in our work based on gamma correction and gradient based technique for contrast enhancement. We leverage the Res-UNet architecture for image segmentation and introduce a novel deep learning network for classification, targeting improved accuracy and precision in diagnostic performance. A Res-UNet segmentation model was trained using 704 chest X-ray images sourced from the Montgomery County and Shenzhen Hospital datasets. Following training, the model was applied to segment lung regions in 1400 chest X-ray scans, encompassing both TB cases and normal controls, obtained from the National Institute of Allergy and Infectious Diseases (NIAID) TB Portal program dataset. The segmented lung regions were subsequently classified as either TB or normal using a deep learning model. A gradient based technique was used for contrast enhancement by capturing intensity changes in image by comparing each pixel with its neighbour with pyramid reduction unique mapping and histogram matching along with gamma correction is used. This integrated approach of segmentation and classification aims to enhance the accuracy and precision of TB detection in chest X-ray images. Classification of segmented images was done using customised convolutional neural network, and visualisation was done using Grad-CAM. The Res-UNet model demonstrated excellent performance for segmentation, achieving an accuracy of 98.18%, recall of 98.40%, precision of 97.45%, F1-score of 97.97%, Dice coefficient of 96.33%, and Jaccard index of 96.05%. Similarly, the classification model exhibited outstanding results, with a classification accuracy of 99.45%, precision of 99.29%, recall of 99.29%, F1-score of 99.29%, and an AUC of 99.9%. Enhanced gradient based method showed ambe of 16.51, entropy of 6.7370, CII of 86.80, psnr of 28.71, ssim of 86.83 which are quite satisfactory. The findings demonstrate the efficiency of our system in diagnosing TB from chest X-rays, potentially surpassing clinician-level precision. This underscores its effectiveness as a diagnostic tool, particularly in resourcelimited settings with restricted access to radiological expertise. Additionally, the modified Res-UNet model demonstrated superior performance compared to the standard U-Net, highlighting its potential for achieving greater diagnostic accuracy.

Bone metastasis has a significant influence on the prognosis of prostate cancer(PCa) patients. In this review, we discussed the current application of PCa bone metastasis diagnosis with single-photon emission computed tomography (SPECT) and positron emission tomography/computed tomography (PET/CT) computer-aided diagnosis(CAD) systems. A literature search identified articles concentrated on PCa bone metastasis and PET/CT or SPECT CAD systems using the PubMed database. We summarized the previous studies focused on CAD systems and manual quantitative markers calculation, and the coincidence rate was acceptable. We also analyzed the quantification methods, advantages, and disadvantages of CAD systems. CAD systems can detect abnormal lesions of PCa patients’ 99mTc-MDP-SPECT, 18F-FDG-PET/CT, 18F-NaF-PET/CT, and 68 Ga-PSMA PET/CT images automated or semi-automated. CAD systems can also calculate the quantitative markers, which can quantify PCa patients’ whole-body bone metastasis tumor burden accurately and quickly and give a standardized and objective result. SPECT and PET/CT CAD systems are potential tools to monitor and quantify bone metastasis lesions of PCa patients simply and accurately, the future clinical application of CAD systems in diagnosing PCa bone metastasis lesions is necessary and feasible.

Computer-aided diagnosis (CAD) systems in mammography have been developed and investigated for several years and recently they have left the research stage to enter the clinical stage. The purpose of this article is to review the present situation of mammography for breast cancer detection and the role played by CAD systems. Results from the recent literature show that CAD systems have the potential to improve the sensitivities of radiologists in the detection of malignant clustered microcalcifications and masses, while keeping specificities at acceptable levels. This leads to the conclusion that CAD systems can be incorporated into clinical practice as a double reading option to radiologists. However, some issues have yet to be tackled for CAD systems to gain better acceptance and more widespread use worldwide.

ObjectiveWe wanted to investigate the usefulness of a computer-aided diagnosis (CAD) system in assisting radiologists to diagnosis malignant solitary pulmonary nodules (SPNs), as compared with diagnosing SPNs with using direct personal drawing.Materials and MethodsForty patients with SPNs were analyzed. After the pre-contrast scan was performed, an additional ten series of post-contrast images were obtained at 20-second intervals. Two investigators measured the attenuation values of the SPNs: a radiologist who drew the regions of interest (ROIs), and a technician who used a CAD system. The Bland and Altman plots were used to compare the net enhancement between a CAD system and direct personal drawing. The diagnostic characteristics of the malignant SPNs were calculated by considering the CAD and direct personal drawing and with using Fisher's exact test.ResultsOn the Bland and Altman plot, the net enhancement difference between the CAD system and direct personal drawing was not significant (within ± 2 standard deriation). The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy of diagnosing malignant SPNs using CAD was 92%, 85%, 75%, 96% and 88%, respectively. The sensitivity, specificity, PPV, NPV and accuracy of diagnosing malignant SPNs using direct drawing was 92%, 89%, 79%, 92% and 88%, respectively.ConclusionThe CAD system was a useful tool for diagnosing malignant SPNs.