Meta Learning based Pneumonia detection using stacking ensemble of CNN Snapshots

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.

Background: Tuberculosis (TB) is a bacterial disease that mainly affects the lungs, but it can also impact other parts of the body, such as the brain, bones, and kidneys. The disease is caused by a bacterium called Mycobacterium tuberculosis and spreads through the air when an infected person coughs or sneezes. TB can be inactive or active; in its active state, noticeable symptoms appear, and it can be transmitted to others. There are ongoing challenges in fighting TB, including resistance to medications, co-infections, and limited resources in areas heavily affected by the disease. These issues make it challenging to eradicate TB. Objective: Timely and precise diagnosis is essential for effective control, especially since TB often goes undetected and untreated, particularly in remote and under-resourced locations. Chest X-ray (CXR) images are commonly used to diagnose TB. However, difficulties can arise due to unusual findings on X-rays and a shortage of radiologists in high-infection areas. Method: To address these challenges, a computer-aided diagnosis (CAD) system that uses the vision transformer (ViT) technique has been developed to accurately identify TB in CXR images. This innovative hybrid CAD approach combines ViT with Principal Component Analysis (PCA) and machine learning (ML) techniques for TB classification, introducing a new method in this field. In the hybrid CAD system, ViT is used for deep feature extraction as a base model, PCA is used to reduce feature dimensions, and various ML methods are used to classify TB. This system allows for quickly identifying TB, enabling timely medical action and improving patient outcomes. Additionally, it streamlines the diagnostic process, reducing time and costs for patients and lessening the workload on healthcare professionals. The TB chest X-ray dataset was utilized to train and evaluate the proposed CAD system, which underwent pre-processing techniques like resizing, scaling, and noise removal to improve diagnostic accuracy. Results: The performance of our CAD model was assessed against existing models, yielding excellent results. The model achieved remarkable metrics: an average precision of 99.90%, recall of 99.52%, F1-score of 99.71%, accuracy of 99.84%, false negative rate (FNR) of 0.48%, specificity of 99.52%, and negative predictive value (NPV) of 99.90%. Conclusions: This evaluation highlights the superior performance of our model compared to the latest available classifiers.

Development of a computer-aided tool for detection of COVID-19 pneumonia from CXR images using machine learning algorithm

In the last century, we have passed two severe pandemics; the 1957 influenza (Asian flu) pandemic and the 1918 influenza (Spanish flu) pandemic with a high fatality rate. In the last few months, we have been again facing a new epidemic (COVID-19), which is a frighteningly high-risk disease and is globally threatening human lives. Among all attempts and presented solutions to tackle the COVID-19, a publicly available dataset of radiological imaging using chest radiography, also called chest X-ray (CXR) images, could efficiently accelerate the detection process of patients infected with COVID-19 through presented abnormalities in their chest radiography images. In this study, we have proposed a deep neural network (DNN), namely RAM-Net, a new combination of MobileNet with Dilated Depthwise Separable Convolution (DDSC), Residual blocks, and Attention augmented convolution. The network has been learned and validated using the COVIDx dataset, one of the most popular public datasets comprising the chest X-ray (CXR) images. Using this model, we could accurately identify the positive cases of COVID-19 viral infection while a new suspicious chest X-ray image is shown to the network. Our network's overall accuracy on the COVIDx test dataset was 95.33%, with a sensitivity and precision of 92% and 99% for COVID-19 cases, respectively, which are the highest results on the COVIDx dataset to date, to the best of our knowledge. Finally, we performed an audit on RAM-Net based on the Grad-CAM's interpretation to demonstrate that our proposed architecture detects SARS-CoV-2 (COVID-19) viral infection by focusing on vital factors rather than relying on irrelevant information.

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.

Radiologists' eye movements during medical image interpretation reflect their perceptual-cognitive processes of diagnostic decisions. The eye movement data can be modeled to represent clinically relevant regions in a medical image and potentially integrated into an artificial intelligence (AI) system for automatic diagnosis in medical imaging. In this article, we first conduct a large-scale eye-tracking study involving 13 radiologists interpreting 191 chest X-ray (CXR) images, establishing a best-of-its-kind CXR visual saliency benchmark. We then perform analysis to quantify the reliability and clinical relevance of saliency maps (SMs) generated for CXR images. We develop CXR image saliency prediction method (CXRSalNet), a novel saliency prediction model that leverages radiologists' gaze information to optimize the use of unlabeled CXR images, enhancing training and mitigating data scarcity. We also demonstrate the application of our CXR saliency model in enhancing the performance of AI-powered diagnostic imaging systems.

The occurrence of COVID-19 disease and the pandemic has caused severe detrimental effects on global health, economy, and well-being. Ignorance and disobeying precautionary measures result in the rapid spread of this contagious infection and subsequent illnesses. There have been more than forty crore cases and fifty lakh deaths reported worldwide. Though the RT-PCR manual testing is widely carried out for diagnosing the presence of the virus in the human body, it is less reliable than chest CT and X-Ray imaging techniques. These tests are swift and assist in determining the severity of the infection with affordable costs. Hence, recent advances involve the application of artificial intelligence and deep learning techniques for automatic detection of this disease. Various datasets of X-Ray images of the chest are available online which are utilized for training and validation of results. In this paper, a dense layer Convolutional Neural Network with one twenty one layers model is used to predict the pneumonia type from the Chest X-Ray images. The results acquired from the implementation show that the model has the highest accuracy of 0.9738 while the specificity is 93.6%, and it outperforms the other similar models. Hence, the possibility of a false positive case occurring is lower and can facilitate easy diagnosis of the infection.

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.

Pneumonia, a severe lung infection caused by various viruses, presents significant challenges in diagnosis and treatment due to its similarities with other respiratory conditions. Additionally, the need to protect patient privacy complicates the sharing of sensitive clinical data. This study introduces FLPneXAINet, an effective framework that combines federated learning (FL) with deep learning (DL) and explainable AI (XAI) to securely and accurately predict pneumonia using chest X-ray (CXR) images. We utilized a benchmark dataset from Kaggle, comprising 8,402 CXR images (3,904 normal and 4,498 pneumonia). The dataset was preprocessed and augmented using a cycle-consistent generative adversarial (CycleGAN) network to increase the volume of training data. Three pre-trained DL models named VGG16, NASNetMobile, and MobileNet were employed to extract features from the augmented dataset. Further, four ensemble DL (EDL) models were used to enhance feature extraction. Feature optimization was performed using recursive feature elimination (RFE), analysis of variance (ANOVA), and random forest (RF) to select the most relevant features. These optimized features were then inputted into machine learning (ML) models, including K-nearest neighbor (KNN), naive bayes (NB), support vector machine (SVM), and RF, for pneumonia prediction. The performance of the models was evaluated in a FL environment, with the EDL network achieving the best results: accuracy 97.61%, F1 score 98.36%, recall 98.13%, and precision 98.59%. The framework's predictions were further validated using two XAI techniques-Local Interpretable Model-Agnostic Explanations (LIME) and Grad-CAM. FLPneXAINet offers a robust solution for healthcare professionals to accurately diagnose pneumonia, ensuring timely treatment while safeguarding patient privacy.

Novel coronavirus disease (COVID-19) is an extremely contagious and quickly spreading coronavirus infestation. Severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), which outbreak in 2002 and 2011, and the current COVID-19 pandemic are all from the same family of coronavirus. This work aims to classify COVID-19, SARS, and MERS chest X-ray (CXR) images using deep convolutional neural networks (CNNs). To the best of our knowledge, this classification scheme has never been investigated in the literature. A unique database was created, so-called QU-COVID-family, consisting of 423 COVID-19, 144 MERS, and 134 SARS CXR images. Besides, a robust COVID-19 recognition system was proposed to identify lung regions using a CNN segmentation model (U-Net), and then classify the segmented lung images as COVID-19, MERS, or SARS using a pre-trained CNN classifier. Furthermore, the Score-CAM visualization method was utilized to visualize classification output and understand the reasoning behind the decision of deep CNNs. Several deep learning classifiers were trained and tested; four outperforming algorithms were reported: SqueezeNet, ResNet18, InceptionV3, and DenseNet201. Original and preprocessed images were used individually and all together as the input(s) to the networks. Two recognition schemes were considered: plain CXR classification and segmented CXR classification. For plain CXRs, it was observed that InceptionV3 outperforms other networks with a 3-channel scheme and achieves sensitivities of 99.5%, 93.1%, and 97% for classifying COVID-19, MERS, and SARS images, respectively. In contrast, for segmented CXRs, InceptionV3 outperformed using the original CXR dataset and achieved sensitivities of 96.94%, 79.68%, and 90.26% for classifying COVID-19, MERS, and SARS images, respectively. The classification performance degrades with segmented CXRs compared to plain CXRs. However, the results are more reliable as the network learns from the main region of interest, avoiding irrelevant non-lung areas (heart, bones, or text), which was confirmed by the Score-CAM visualization. All networks showed high COVID-19 detection sensitivity (> 96%) with the segmented lung images. This indicates the unique radiographic signature of COVID-19 cases in the eyes of AI, which is often a challenging task for medical doctors.

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.

The Coronavirus was detected in Wuhan, China in late 2019 and then led to a\npandemic with a rapid worldwide outbreak. The number of infected people has\nbeen swiftly increasing since then. Therefore, in this study, an attempt was\nmade to propose a new and efficient method for automatic diagnosis of Corona\ndisease from X-ray images using Deep Neural Networks (DNNs). In the proposed\nmethod, the DensNet169 was used to extract the features of the patients' Chest\nX-Ray (CXR) images. The extracted features were given to a feature selection\nalgorithm (i.e., ANOVA) to select a number of them. Finally, the selected\nfeatures were classified by LightGBM algorithm. The proposed approach was\nevaluated on the ChestX-ray8 dataset and reached 99.20% and 94.22% accuracies\nin the two-class (i.e., COVID-19 and No-findings) and multi-class (i.e.,\nCOVID-19, Pneumonia, and No-findings) classification problems, respectively.\n

COVID-19 has engulfed over 200 nations through human-to-human transmission, either directly or indirectly. Reverse Transcription-polymerase Chain Reaction (RT-PCR) has been endorsed as a standard COVID-19 diagnostic procedure but has caveats such as low sensitivity, the need for a skilled workforce, and is time-consuming. Coronaviruses show significant manifestation in Chest X-Ray (CX-Ray) images and, thus, can be a viable option for an alternate COVID-19 diagnostic strategy. An automatic COVID-19 detection system can be developed to detect the disease, thus reducing strain on the healthcare system. This paper discusses a real-time Convolutional Neural Network (CNN)based system for COVID-19 illness prediction from CX-Ray images on the cloud. The implemented CNN model displays exemplary results, with training accuracy being 99.94% and validation accuracy reaching 98.81%. The confusion matrix was utilized to assess the models' outcome and achieved 99% precision, 98% recall, 99% F1 score, 100% training area under the curve (AUC) and 98.3% validation AUC. The same CX-Ray dataset was also employed to predict the COVID-19 disease with deep Convolution Neural Networks (DCNN), such as ResNet50, VGG19, InceptonV3, and Xception. The prediction outcome demonstrated that the present CNN was more capable than the DCNN models. The efficient CNN model was deployed to the Platform as a Service (PaaS) cloud.

We developed a rich dataset of Chest X-Ray (CXR) images to assist investigators in artificial intelligence. The data were collected using an eye-tracking system while a radiologist reviewed and reported on 1,083 CXR images. The dataset contains the following aligned data: CXR image, transcribed radiology report text, radiologist’s dictation audio and eye gaze coordinates data. We hope this dataset can contribute to various areas of research particularly towards explainable and multimodal deep learning/machine learning methods. Furthermore, investigators in disease classification and localization, automated radiology report generation, and human-machine interaction can benefit from these data. We report deep learning experiments that utilize the attention maps produced by the eye gaze dataset to show the potential utility of this 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.