Detection Of Lung Disease Research Articles

Lung Function Monitoring Using Machine Learning

This project aims to develop a system equipped with machine learning algorithm capable of continuously monitoring respiratory conduct. The system utilizes machine learning to interpret pulmonary function tests, lift the management of respiratory diseases, and potentially deliver improvements in the diagnosis and treatment of various disease states in pulmonary and critical care medicine. Additionally, the system automatically predicts lung function based on acoustic signals from coughing and wheezing, enabling noninvasive monitoring of asthma severity. The project also seeks to forecast lung age and improve the current dataset of audio samples to enhance the accuracy and reliability of the results. The application of machine learning in pulmonary function testing holds significant potential for remote monitoring of high risk patients and the early detection and treatment of lung diseases.

Deep Ensemble Learning Model for Diagnosis of Lung Diseases from Chest X -Ray Images

Objectives: This study aims to develop a robust medical recognition system using deep learning for the identification of various lung diseases, including COVID-19, pneumonia, lung opacity, and normal states, from chest X-ray images. The focus is on implementing ensemble fixed features learning methods to enhance diagnostic capabilities, contributing to the development of a cost-effective and reliable diagnostic tool for combating the global epidemic of lung disorders. Methods: The study utilizes a Kaggle dataset containing COVID-19 chest radiography images. Raw X-ray images undergo preprocessing for contrast enhancement and noise removal while addressing dataset imbalance through near-miss resampling. Ensemble learning techniques, including two and three-level methods, are employed to harness the strengths of individual base learners—VGG16, InceptionV3, and MobileNetV2. The model's performance is evaluated using metrics such as accuracy, recall, precision, and F1-score. For remote access, a user interface and a shared web link are developed using Python Gradio. Findings: In two-level ensembles, features from base learners are concatenated and classified using a support vector machine. Three-level ensembles use concatenated features classified by three machine learning classifiers, employing a majority voting system for the final prediction. The two-level method achieved 93% accuracy, precision, recall, and F1 score. The three-level ensemble model demonstrates superior performance, achieving 94% accuracy in detecting four lung diseases, namely COVID-19, pneumonia, lung opacity, and normal states. Novelty: This research contributes to the field by showcasing the efficacy of deep learning technology, particularly ensemble learning, in enhancing the detection of lung diseases from raw chest X-ray images. The model employs three modified and efficient pretrained networks for automatic feature extraction, eliminating the need for manual feature engineering. The developed model stands as a promising decision-support tool for healthcare professionals, particularly in low-resource environments. Keywords: Convolutional Neural Network (CNN), Deep Learning (DL), Transfer Learning (TL), Ensemble learning (EL), Fixed feature extraction, Chest X­rays (CXR), Lung diseases

Multiple-breath washout to detect lung disease in patients with inborn errors of immunity.

Pulmonary manifestations are the major cause of morbidity and mortality in patients with inborn errors of immunity (IEI). New and more sensitive diagnostic methods can potentially lead to earlier recognition and treatment of IEI lung disease and improve outcome. The aim of this study was to compare multiple-breath washout (MBW) and spirometry in patients with IEI and cystic fibrosis (CF) as well as healthy controls (HC) and to evaluate the sensitivity of lung clearance index (LCI) to assess lung disease in IEI. IEI patients (n=114) were recruited from our paediatric and adult immunodeficiency outpatient clinics and compared to age-matched CF patients (n=114) and HC (n=114). MBW measurements and spirometry were performed in the study participants, and MBW testing was repeated after 63-707 days in IEI patients (n=70). The LCI was significantly higher in IEI patients than in HC (p<0.001) and significantly lower than in CF patients (p<0.001). The forced expiratory volume in 1 s (FEV1) z-score was significantly lower in IEI patients than in HC (p<0.01) and significantly higher than in CF patients (p<0.01). LCI and FEV1 z-score correlated moderately negatively in the total cohort, the IEI group and the CF group. Nineteen (20.7%) of 92 IEI patients and 35 (33.3%) of 105 CF patients had an elevated LCI but a normal FEV1 z-score. After a median of 364 days, the median LCI of 70 IEI patients increased significantly by 0.2. MBW is useful to detect lung disease in IEI and is more sensitive than spirometry.

Utilization of Spiking Neural Network (SNN) in X-Ray Image for Lung Disease Detection

The large number of cases of lung disease means that doctors have difficulty in making initial diagnoses, making them prone to misdiagnoses. One type of lung disease that is included in the vulnerable category is pneumonia. Early detection of the condition of the lungs affected by bacterial pneumonia can be carried out by screening using the X-Ray examination modality, namely Digital Radiography (DR). However, in practice, the diagnosis process on Citra DR takes a long time because it requires competent medical personnel (specialists). A system is needed that can help medical personnel to speed up the process of diagnosing lung disease and get accurate results so that misdiagnosis does not occur. The aim of this research is to utilize the Spiking Neural Network (SNN) method for classifying lung disease from DR images. The system was created using MATLAB with the initial step of creating a read data program, namely reading DR image secondary data in .jpg format taken from Kaggle.com. This research uses DR image data totaling 200 images. Next, standardize the size to 50 x 50 pixels. Then segmenting the image divides the gray level histogram into two different parts of the image automatically without requiring user assistance to enter threshold values ​​for normal and pneumonia images. Then convert the image to 1 dimension and create a manual program for the training data using 50 normal images and 50 pneumonia images. Lastly, create a program to test the data using 100 normal images and 100 pneumonia images. Based on the results of data testing, a confusion matrix was obtained from 200 images with sensitivity of 87%, specificity of 69%, precision of 73.7288%, recall of 69%, and accuracy of 78%

Grating-based x-ray dark-field CT for lung cancer diagnosis in mice

BackgroundThe low absorption of x-rays in lung tissue and the poor resolution of conventional computed tomography (CT) limits its use to detect lung disease. However, x-ray dark-field imaging can sense the scattered x-rays deflected by the structures being imaged. This technique can facilitate the detection of small alveolar lesions that would be difficult to detect with conventional CT. Therefore, it may provide an alternative imaging modality to diagnose lung disease at an early stage.MethodsEight mice were inoculated with lung cancers simultaneously. Each time two mice were scanned using a grating-based dark-field CT on days 4, 8, 12, and 16 after the introduction of the cancer cells. The detectability index was calculated between nodules and healthy parenchyma for both attenuation and dark-field modalities. High-resolution micro-CT and pathological examinations were used to crosscheck and validate our results. Paired t-test was used for comparing the ability of dark-field and attenuation modalities in pulmonary nodule detection.ResultsThe nodules were shown as a signal decrease in the dark-field modality and a signal increase in the attenuation modality. The number of nodules increased from day 8 to day 16, indicating disease progression. The detectability indices of dark-field modality were higher than those of attenuation modality (p = 0.025).ConclusionsCompared with the standard attenuation CT, the dark-field CT improved the detection of lung nodules.Relevance statementDark-field CT has a higher detectability index than conventional attenuation CT in lung nodule detection. This technique could improve the early diagnosis of lung cancer.Key points• Lung cancer progression was observed using x-ray dark-field CT.• Dark-field modality complements with attenuation modality in lung nodule detection.• Dark-field modality showed a detectability index higher than that attenuation in nodule detection.Graphical

A Systematic Survey of Automatic Detection of Lung Diseases from Chest X-Ray Images: COVID-19, Pneumonia, and Tuberculosis

A Systematic Survey of Automatic Detection of Lung Diseases from Chest X-Ray Images: COVID-19, Pneumonia, and Tuberculosis

Developing the Lung Graph-Based Machine Learning Model for Identification of Fibrotic Interstitial Lung Diseases

Accurate detection of fibrotic interstitial lung disease (f-ILD) is conducive to early intervention. Our aim was to develop a lung graph-based machine learning model to identify f-ILD. A total of 417 HRCTs from 279 patients with confirmed ILD (156 f-ILD and 123 non-f-ILD) were included in this study. A lung graph-based machine learning model based on HRCT was developed for aiding clinician to diagnose f-ILD. In this approach, local radiomics features were extracted from an automatically generated geometric atlas of the lung and used to build a series of specific lung graph models. Encoding these lung graphs, a lung descriptor was gained and became as a characterization of global radiomics feature distribution to diagnose f-ILD. The Weighted Ensemble model showed the best predictive performance in cross-validation. The classification accuracy of the model was significantly higher than that of the three radiologists at both the CT sequence level and the patient level. At the patient level, the diagnostic accuracy of the model versus radiologists A, B, and C was 0.986 (95% CI 0.959 to 1.000), 0.918 (95% CI 0.849 to 0.973), 0.822 (95% CI 0.726 to 0.904), and 0.904 (95% CI 0.836 to 0.973), respectively. There was a statistically significant difference in AUC values between the model and 3 physicians (p < 0.05). The lung graph-based machine learning model could identify f-ILD, and the diagnostic performance exceeded radiologists which could aid clinicians to assess ILD objectively.Graphical Given a sequence of HRCT slices from a patient, the lung field is first automatically extracted. Next, this lung region is divided into 36 sub-regions using geometric rules, obtaining a lung atlas. And then, the lung graph is built based on 3D radiomics features of each sub-region of the lung atlas. Finally, the model’s predictions were compared to the physicians’ assessment results.

Lung disease detection using EasyNet

One of the major causes of deaths worldwide is the pulmonary diseases. There is an increasing need for an efficient technique that can automatically diagnose these diseases with high accuracy. In this paper, we propose a deep learning architecture to automatically detect pulmonary diseases. The raw pulmonary sound signals are taken from two popular datasets: ICBHI and KAUH datasets. These signals have diverse sampling frequencies of 4 kHz, 10 kHz or 44.1 kHz. The signals from KAUH dataset have a duration of minimum 5 s, while ICBHI signals have a duration of 10 to 90 s. These signals undergo pre-processing, which involves re-sampling them to a common 4 kHz frequency, and segmenting them into frames lasting 3 s. The frames are then normalized and passed to the proposed EasyNet model for training and classification. The EasyNet architecture contains only two convolution layers, which reduces the model complexity. The model’s performance is analyzed for both binary detection as well as multi-class detection. Our method performs well in all the considered evaluation scenarios, and yields an accuracy, sensitivity, and specificity of 1.0 for the KAUH dataset, while for the ICBHI dataset, an accuracy of 0.997, sensitivity of 0.999, and specificity of 0.997 is achieved. For the combined dataset, we have achieved an accuracy of 0.998, with a sensitivity and specificity of 0.999. These values are better than the existing state-of-the-art methods. The proposed architecture is quite simple yet effective in detecting lung diseases.

A CONV-RFDNN Model for the Classification and Detection of Lung Diseases on Chest X-Rays Using Transfer Learning

A CONV-RFDNN Model for the Classification and Detection of Lung Diseases on Chest X-Rays Using Transfer Learning

AFEX-Net: Adaptive feature extraction convolutional neural network for classifying computerized tomography images.

Deep convolutional neural networks are favored methods that are widely used in medical image processing due to their demonstrated performance in this area. Recently, the emergence of new lung diseases, such as COVID-19, and the possibility of early detection of their symptoms from chest computerized tomography images has attracted many researchers to classify diseases by training deep convolutional neural networks on lung computerized tomography images. The trained networks are expected to distinguish between different lung indications in various diseases, especially at the early stages. The purpose of this study is to introduce and assess an efficient deep convolutional neural network, called AFEX-Net, that can classify different lung diseases from chest computerized tomography images. We designed a lightweight convolutional neural network called AFEX-Net with adaptive feature extraction layers, adaptive pooling layers, and adaptive activation functions. We trained and tested AFEX-Net on a dataset of more than 10,000 chest computerized tomography slices from different lung diseases (CC dataset), using an effective pre-processing method to remove bias. We also applied AFEX-Net to the public COVID-CTset dataset to assess its generalizability. The study was mainly conducted based on data collected over approximately six months during the pandemic outbreak in Afzalipour Hospital, Iran, which is the largest hospital in Southeast Iran. AFEX-Net achieved high accuracy and fast training on both datasets, outperforming several state-of-the-art convolutional neural networks. It has an accuracy of and on the CC and COVID-CTset datasets, respectively, with a learning speed that is 3 times faster compared to similar methods due to its lightweight structure. AFEX-Net was able to extract distinguishing features and classify chest computerized tomography images, especially at the early stages of lung diseases. The AFEX-Net is a high-performing convolutional neural network for classifying lung diseases from chest CT images. It is efficient, adaptable, and compatible with input data, making it a reliable tool for early detection and diagnosis of lung diseases.

Usefulness of Quantification and Serial Monitoring of Fine Crackles for Early Detection of Treatment-related Lung Injury: A Report of Two Cases.

Early detection and appropriate management of treatment-related interstitial lung disease (ILD) are important in cancer treatment. We established an algorithm for quantifying fine crackles using machine learning and reported that the fine crackle quantitative value (FCQV) calculated by this algorithm was more sensitive than chest radiography for detecting interstitial changes. Using this algorithm, we periodically analyzed respiratory sounds in two patients with lung cancer who developed treatment-related ILDs and found that the FCQV was elevated before the diagnosis of ILD. These cases may indicate the usefulness of the FCQV in the early diagnosis of treatment-related ILDs.

ViT-ILD: A Vision Transformer-based Neural Network for Detection of Interstitial Lung Disease from CT Images

Interstitial Lung Disease (ILD) is a lung illness characterized by inflammation and scarring. Identifying and categorizing ILD patterns using chest Computed Tomography (CT) images is crucial for diagnosis and treatment planning. Deep learning and computer vision advancements offer the potential for automating medical image examination, such as the transformer model, which identifies intricate dependencies and relationships in data. Chest CT scans provide valuable information for ILD pattern classification and diagnosis. The Vision Transformer (ViT) based Multi-Head Self Attention (MHSA) architecture detects local and global spatial dependencies, focusing on relevant regions and considering contextual interactions. The ViT-based model architecture aims to categorize ILD patterns using MHSA mechanisms. The proposed ViT-ILD model improves the performance by modifying hyperparameters, attention heads, and hidden units. It also utilises techniques of residual connections, layer normalization, and positional encoding for improvement. The proposed method ViT-ILD has achieved comparable training, validation and test accuracy of 100%, 98%, and 82.75% respectively for the 4-class classification with a healthy lung, hypersensitivity pneumonitis, pulmonary fibrosis, and tuberculosis from the MedGift CT dataset. It is observed that the proposed ViT-ILD model has achieved test accuracy, recall, precision, and f1-score of 82.75%, 74.15%, 100%, and 82.35%.

An Extensive Analysis of Machine Learning Techniques With Hyper-Parameter Tuning by Bayesian Optimized SVM Kernel for the Detection of Human Lung Disease

An Extensive Analysis of Machine Learning Techniques With Hyper-Parameter Tuning by Bayesian Optimized SVM Kernel for the Detection of Human Lung Disease

Improving Early Detection and Classification of Lung Diseases With Innovative MobileNetV2 Framework

Improving Early Detection and Classification of Lung Diseases With Innovative MobileNetV2 Framework

Dual interactive Wasserstein generative adversarial network optimised with remora optimisation algorithm-based lung disease detection using chest X-ray images

Dual interactive Wasserstein generative adversarial network optimised with remora optimisation algorithm-based lung disease detection using chest X-ray images

Automated lung disease detection, classification and prediction using RNN framework

Lung diseases are widespread throughout the globe. This group of illnesses includes chronic obstructive pulmonary disease, pneumonia, asthma, TB, fibrosis, and others. The earliest possible diagnosis of lung illness is crucial. Numerous image processing and artificial intelligence models have been created with this goal in mind. Several types of research have been initiated around the world since the arrival of the novel Covid-19 for its reliable estimation. The earlier respiratory disease pneumonia is linked to Covid-19 because several patients died as a result of severe chest congestion (pneumonic condition). Medical experts find it pulmonary illnesses caused by pneumonia and Covid-19 are difficult to differentiate. Chest CT-Scan imaging is the most accurate approach for predicting lung disease. Recently, a number of academics reported using AI-based methods to classify medical images using training data from CT scans. Deep learning is a very effective technique for understanding difficult cognitive difficulties, and more and more challenges are using and evaluating it. Recurrent neural network method, a deep learning system that can accurately detect COVID from CT-scan pictures, was employed in this study. Detect various lung illnesses like pneumonia and TB by using Multi-class RNN. The experimental findings demonstrate the suggested approach increases the precision of disease prediction and also gives information on the diagnoses of the illnesses under study.

Automated Detection of Lung Diseases (covid-19) Based on X-ray Images Using A Deep Learning Approach

Automated Detection of Lung Diseases (covid-19) Based on X-ray Images Using A Deep Learning Approach

Enhanced deep transfer learning with multi-feature fusion for lung disease detection

Enhanced deep transfer learning with multi-feature fusion for lung disease detection

Transthoracic ultrasonography in the assessment of interstitial lung disease in patients with systemic sclerosis

Background: Transthoracic ultrasonography was considered, sometimes in the past as unsuitable for the assessment of lung but it eventually emerged as an important tool in the diagnosis and in guiding treatment of various lung and pleural diseases. Several studies in the past proved it to be a useful complementary tool in the detection of interstitial pulmonary fibrosis. Aims and Objectives: This study aims to assess the utility of transthoracic uultrasonography (TUS) in comparison to high-resolution computed tomography (HRCT) thorax in the detection of interstitial lung disease (ILD) in systemic sclerosis (SSc) patients. Materials and Methods: SSc patients with HRCT proven ILD were identified and classified according to Warrick’s score. Sonographic evidence of ILD in these patients were judged by the presence of B-lines or ultrasonic lung comets (ULCs). Each procedure was studied and interpreted by two independent examiners blinded about the patient’s clinical and radiological features. Standard spirometric measurements were performed in all patients. Results: A highly significant positive linear correlation between ULCs and Warrick’s score was found in this study (r=0.83, P≤0.001). ULC score had moderate negative correlation with forced expiratory volume in 1 s (FEV1) % p (r=−0.60, P=0.001) and weak negative correlation with forced vital capacity (FVC) % p (r=−0.45, P=0.01) and FEV1/FVC % (r=−0.42, P=0.02). Conclusion: USG lung can be suggested as a useful non-invasive tool in the detection of ILDs in scleroderma patients.

Performance Evaluation of Bagging Meta-Estimator in Lung Disease Detection: A Case Study on Imbalanced Dataset

In this study, titled "Performance Evaluation of Bagging Meta-Estimator in Lung Disease Detection: A Case Study on Imbalanced Dataset," we explore the effectiveness of the Bagging Meta-Estimator in diagnosing lung diseases, focusing on the challenges of imbalanced datasets. Utilizing a dataset segmented and characterized by Hu moments and encompassing categories of Normal, Bacterial Pneumonia, and Tuberculosis, the algorithm's performance was assessed through a 5-fold cross-validation. Results indicated moderate effectiveness with an average accuracy of 60.574%, precision of 60.749%, recall of 59.753%, and F1-Score of 59.416%, highlighting variable performance across folds. These findings suggest that while the Bagging Meta-Estimator has potential in medical imaging, further refinement is needed for consistent and reliable lung disease detection, especially in imbalanced datasets.