Auto encoder-guided Feature Extraction for Pneumonia Identification from Chest X-ray Images

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.

COVID-19, also known as the coronavirus disease 2019 is a highly contagious respiratory illness caused by SARS-COV-2 virus which spread form china globally resulting in pandemic. With advances in diagnostic tools, radiologic imaging is widely used for COVID-19 pneumonia diagnosis other than usual clinical and laboratory testing. In this paper, several deep learning and machine learning enhanced techniques are applied to X-Ray and CT-Scan medical images for the detection of covid-19 and also a clinical data for the prediction of the covid19. The images are preprocessed and trained using U-Net model, a popular architecture for image segmentation tasks. The accuracy and F1-score were found to be above 98% in the diagnosis of COVID-19 using CT-scan images. Further, transfer learning techniques were applied to overcome the insufficient data and to improve the training time. The binary and multi-class classification of X-ray images tasks were performed by utilizing enhanced CNN deep transfer learning architecture. An accuracy of 99% was achieved by enhanced CNN in the detection of X-ray images from COVID-19 and pneumonia.Further,using clinical dataset we compared the performance of supervised machine learning algorithms: logistic regression, random forest classifier, and XGBoost classifier. These algorithms were trained and evaluated on the preprocessed and feature-selected dataset to predict COVID-19 cases. The results showed that XGBoost classifier outperformed logistic regression and random forest classifier in predicting COVID-19 cases based on symptoms, age, gender, and test indications.

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.

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.

A deep unsupervised saliency model for lung segmentation in chest X-ray images

Detecting and classifying pneumonia in X -ray images involves leveraging advanced technologies like Computer Vision (CV) and Machine Learning (ML) to identify and categorize respiratory ailments. The amalgamation of these methodologies holds the potential to aid healthcare professionals in swiftly recognizing and managing pneumonia cases, facilitating early diagnosis and effective treatment. The use of Deep Learning (DL) for the classification and detection of pneumonia in X -ray images has emerged as a successful approach for automated disease identification. As a subset of Artificial Intelligence (AI), DL concentrates on training neural networks with intricate layers to autonomously learn complex representations and patterns from data. In this study, we employ the Conv -MBDNN model to develop the Automatic Detection and Classification of Pneumonia disease. The proposed approach comprises multiple stages aimed at enhancing accuracy and diagnostic performance. Initially, a pre -processing stage is implemented, involving image resizing and the application of a Wiener Filter (WF) to enhance image quality. Subsequently, the feature extraction is conducted using the MobileNetV3 architecture. Finally, the extracted features are fed into an SVM classifier model to distinguish between different types and severity levels of pneumonia. A comprehensive analysis of experimental results demonstrates that the Conv -MBDNN technique outperforms other recent approaches in terms of performance and accuracy in pneumonia detection and classification using X -ray images.

IntroductionIn swine disease surveillance, obtaining labeled data for supervised learning models can be challenging because many farms lack standardized diagnostic routines and consistent health monitoring systems. Unsupervised learning is particularly suitable in such scenarios because it does not require labeled data, allowing for detecting anomalies without predefined labels. This study evaluates the effectiveness of unsupervised machine learning models in detecting anomalies in productivity indicators in swine breeding herds.MethodsAnomalies, defined as deviations from expected patterns, were identified in indicators such as abortions per 1000 sows, prenatal losses, preweaning mortality, total born, liveborn, culled sows per 1000 sows, and dead sows per 1000 sows. Three unsupervised models - Isolation Forest, Autoencoder, and K-Nearest Neighbors (KNN) - were applied to data from two swine production systems. The herd-week was used as the unit of analysis, and anomaly scores above the 75th percentile were used to flag anomalous weeks. A permutation test assessed differences between anomalous and non-anomalous weeks. Performance was evaluated using F1-score, precision, and recall, with true anomalous weeks defined as those coinciding with reported health challenges, including porcine reproductive and respiratory syndrome (PRRS) and Seneca Valley virus outbreaks. A total of 8,044 weeks were analyzed.ResultsThe models identified 336 anomalous weeks and 1,008 non-anomalous weeks in Production System 1, and 1,675 anomalous weeks and 5,025 non-anomalous weeks in Production System 2. The results from the permutation test revealed significant differences in productivity indicators between anomalous and non-anomalous weeks, especially during PRRS outbreaks, with more subtle changes observed during Seneca Valley virus outbreaks. The models performed well in detecting the PRRSV anomaly, achieving perfect precision (100%) across all models for both production systems. For anomalies like SVV the models showed lower performance compared to PRRSV.DiscussionThese findings suggest that unsupervised machine learning models are promising tools for early disease detection in swine herds, as they can identify anomalies in productivity data that may signal health challenges.

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.

Pneumonia is a very common respiratory infection in low- and middle-income countries and is still a leading cause of death, especially among children under five years old. Modern technologies, such as machine learning, offer significant potential in improving the automatic detection of pneumonia through chest X-ray (CXR) image analysis. This study aims to develop a more accurate pneumonia diagnosis system by evaluating various feature extraction methods. CXR datasets of pneumonia patients were resized to 180x180 pixels and balanced using the SMOTE-Tomek technique. Three main approaches were investigated: direct classification using Support Vector Machine (SVM) on the SMOTE-Tomek balanced dataset, feature extraction using Sobel edge detection followed by SVM classification, and feature extraction using MobileNet-V2 followed by SVM classification. The results showed that Scheme 1 achieved 97% accuracy, Scheme 2 decreased to 95%, and Scheme 3 achieved the highest accuracy at 98%. The lower accuracy in Scheme 2 is due to the limitations of Sobel edge detection, which reduces the key features in the CXR image. On the other hand, the improvement in Scheme 3 is due to the effective feature extraction capability of MobileNet-V2. In conclusion, the choice of feature extraction method plays an important role in determining the accuracy of an automated diagnostic system. This study builds on existing research and is expected to make a significant contribution to the development of more accurate and efficient automated diagnostic systems, which can ultimately help reduce pneumonia-related mortality.

The advent of gravitational wave astronomy (GW) has revolutionized the observation of cataclysmic cosmic events, such as black hole mergers and neutron star collisions. The Laser Interferometer Gravitational-Wave Observatory (LIGO) has been at the forefront of these discoveries. However, the immense volume and complexity of gravitational wave data present significant challenges for traditional analysis methods. This paper investigates the growing synergy between artificial intelligence (AI) and GW science, emphasizing how AI enhances signal detection, noise reduction, and data interpretation. It begins with an overview of GW fundamentals and the role of machine learning in increasing detector sensitivity. Notable GW events observed by LIGO are discussed alongside persistent analytical challenges such as data quality, generalization, and computational constraints. A comprehensive performance review of AI techniques, including supervised learning, unsupervised learning, deep learning, and reinforcement learning is presented based on data spanning 2021 to 2024. Evaluation metrics include accuracy, precision, true positive rate (TPR), false positive rate (FPR), and computational efficiency. Findings indicate that deep learning and supervised learning outperform other approaches, particularly in enhancing TPR and minimizing FPR. While unsupervised and reinforcement learning models offer less precision, they demonstrate high efficiency and potential for real-time applications. The study also explores AI’s integration into next-generation detectors and waveform reconstruction techniques. Overall, the integration of AI into GW research significantly improves the reliability and speed of event detection, unlocking new possibilities for exploring the dynamic universe. This paper provides a comprehensive outlook on the transformative role of AI in shaping the future of GW astronomy.

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.

Chest X-ray imaging is a crucial diagnostic tool in medical practice, used for assessing various respiratory, cardiovascular, and musculoskeletal conditions. Recent advances in machine learning and computer vision have led to the development of algorithms capable of analyzing chest X-ray images to identify signs of disease or abnormalities with high accuracy. This study focuses on the application of a customized WideResNet model, a deep learning architecture, to classify chest X-ray images quality and diagnose various diseases. Two datasets were used in this study: an in-distribution dataset containing 5000 chest X-ray images classified into four classes (Infiltration, Effusion, Atelectasis, and Mass) and an out-of-distribution dataset consisting of chest X-Rays images also classified into four classes (Cardiomegaly, Consolidation, Emphysema, No Findings). Both datasets were preprocessed using a Scharr filter to enhance image quality and reduce noise. The Scharr filter is known for its accuracy in detecting small and diagonal edges, making it a suitable choice for preprocessing chest X-ray images. The WideResNet model, an extension of the ResNet architecture, was employed for image classification. The model's architecture consists of multiple residual blocks stacked together, each containing convolutional layers, batch normalization layers, and ReLU activation functions. The skip connections within the residual blocks address the vanishing gradient problem, allowing the model to learn the identity function and preserve input information. Wide residual blocks, a key feature of the WideResNet model, were used to improve the network's capacity to learn complex features while reducing its depth. The customized WideResNet model was trained and tested on the in-distribution and the out-of-distribution dataset to evaluate its ability to generalize to other medical conditions beyond chest X-rays. The model's performance was assessed by fine-tuning hyperparameters such as network depth, residual block width, and the number of filters in each convolutional layer. This study demonstrates the potential of the WideResNet model, combined with effective preprocessing techniques, in improving the accuracy and speed of chest X-ray imaging for disease diagnosis. The application of deep learning models such as WideResNet in medical imaging can lead to better patient outcomes through faster and more accurate detection of diseases. Furthermore, the use of out-of-distribution datasets for testing showcases the model's robustness and potential applicability in various medical fields beyond chest X-ray imaging.

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.

Introduction: The early automatic diagnosis of the novel coronavirus (COVID-19) disease could be very helpful to reduce its spread around the world. In this study, we revisit the identification of COVID-19 from chest X-ray images using deep learning. Methods: We collected a relatively large COVID-19 dataset—compared with previous studies—containing 309 real COVID-19 chest X-ray images. We also prepared 2,000 chest X-ray images of pneumonia cases and 1,000 images of healthy controls. Deep transfer learning was used to detect abnormalities in our image dataset. We fine-tuned three, pre-trained convolutional neural network (CNN) models on a training dataset: DenseNet 121, NASNetLarge, and NASNet-Mobile. Results: The evaluation of our models on a test dataset showed that these models achieved an average sensitivity rate of approximately 99.45% and an average specificity rate of approximately 99.5%. Conclusion: A larger dataset of COVID-19 X-ray images could lead to more accurate and reliable identification of COVID-19 infections using deep transfer learning. However, the clinical diagnosis of COVID-19 disease is always necessary.