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2018, Methods of information in medicine
Diffuse lung diseases (DLDs) are a diverse group of pulmonary disorders, characterized by inflammation of lung tissue, which may lead to permanent loss of the ability to breathe and death. Distinguishing among these diseases is challenging to physicians due their wide variety and unknown causes. Computer-aided diagnosis (CAD) is a useful approach to improve diagnostic accuracy, by combining information provided by experts with Machine Learning (ML) methods. Exploring the potential of dimensionality reduction combined with ML methods for diagnosis of DLDs; improving the classification accuracy over state-of-the-art methods. A data set composed of 3252 regions of interest (ROIs) was used, from which 28 features were extracted per ROI. We used Principal Component Analysis, Linear Discriminant Analysis, and Stepwise Selection - Forward, Backward, and Forward-Backward to reduce feature dimensionality. The feature subsets obtained were used as input to the following ML methods: Support...
International Journal of Health Sciences (IJHS)
Survey for Lung diseases using machine learning methods2022 •
2022 •
Compared to most other tissues, lungs are directly exposed to oxygen concentrations. Lung diseases are one of the leading causes of death. There are many different lung diseases, some of which are caused by viral, bacterial, or fungal infections. Other lung diseases are associated with environmental factors, including COVID19, tuberculosis, bronchitis, pneumonia, etc. Deep learning has shown great potential when applied to medical images for disease detection including lung disease. We build and compare two pre-trained models, MobileNet and VGG16 architectures using the Transfer learning approach. We have also used Supervised Machine Learning algorithms like Random forest, Decision Trees, Support Vector Machines, and Logistic Regression. This paper provides the analysis which we have performed on the different algorithms.
Computers, Materials & Continua
Non-Invasive Early Diagnosis of Obstructive Lung Diseases Leveraging Machine Learning Algorithms2021 •
The application of contemporary technologies is important to medical progress. To create accurate and specialised treatment choices for a range of ailments, extensive study performed in partnership with researchers, health care professionals, and patients is important. This study aims to identify the degree of accuracy that is acceptable in the medical sector by using deep learning on publicly available data. First, we extracted spectrogram features and labels from the annotated lung sound recordings to feed into our 2D Convolutional Neural Network (CNN) model. In this paper, we solve the problem of medical data scarcity by identifying pulmonary diseases from chest X-Ray pictures using small volume datasets with less than a thousand samples. Several studies have been conducted on the application of deep learning to identify lung disease have been published in the literature. The research goes into the history of deep learning and its applications in pulmonary imaging. Deep learning algorithms are utilised to treat pneumonia, tuberculosis, lung cancer and other lung diseases. A review of various typical deep learning network topologies used in medical image processing is also provided. A taxonomy shows the links between previous work and categorises it based on characteristics that might help readers better understand the topic. Trend analysis, on the other hand, gives an overview of the research direction of the area of interest that has been emphasised in previous work.
International Journal of Engineering and Technology Innovation
Machine Learning-Based Classification of Pulmonary Diseases through Real-Time Lung SoundsThe study presents a computer-based automated system that employs machine learning to classify pulmonary diseases using lung sound data collected from hospitals. Denoising techniques, such as discrete wavelet transform and variational mode decomposition, are applied to enhance classifier performance. The system combines cepstral features, such as Mel-frequency cepstrum coefficients and gammatone frequency cepstral coefficients, for classification. Four machine learning classifiers, namely the decision tree, k-nearest neighbor, linear discriminant analysis, and random forest, are compared. Evaluation metrics such as accuracy, recall, specificity, and f1 score are employed. This study includes patients affected by chronic obstructive pulmonary disease, asthma, bronchiectasis, and healthy individuals. The results demonstrate that the random forest classifier outperforms the others, achieving an accuracy of 99.72% along with 100% recall, specificity, and f1 scores. The study suggest...
Health Informatics Journal
Diagnosing asthma and chronic obstructive pulmonary disease with machine learningThis study examines the clinical decision support systems in healthcare, in particular about the prevention, diagnosis and treatment of respiratory diseases, such as Asthma and chronic obstructive pulmonary disease. The empirical pulmonology study of a representative sample (n = 132) attempts to identify the major factors that contribute to the diagnosis of these diseases. Machine learning results show that in chronic obstructive pulmonary disease’s case, Random Forest classifier outperforms other techniques with 97.7 per cent precision, while the most prominent attributes for diagnosis are smoking, forced expiratory volume 1, age and forced vital capacity. In asthma’s case, the best precision, 80.3 per cent, is achieved again with the Random Forest classifier, while the most prominent attribute is MEF2575.
International journal of health sciences
Prediction of lung disease using machine and deep learning techniquesNowadays lung diseases are becoming a significant problem. In spite of this, Corona virus disease 2019 (COVID-19) has become a pandemic all over the world from last two years which effected lungs of few patients also. Many people are suffering from lungs diseases like Asthma, Allergies, lung cancer etc. The patients whose lung gets affected due to COVID-19 may face some lungs diseases in near future, so it very significant to early diagnosis of lungs diseases to save human life. Machine learning (ML) with feature selection techniques play significant role in the medical field by making diseases diagnoses accurate and early. The objective of this paper is to presents a review of recent ML algorithms and feature selection techniques used to predict lung diseases. As we cover the study between 2020 – 2021, some supervised (SVM, Logistic Regression, Random Forest, Logistic model tree, Bayesian Networks) machine learning techniques on 18,253 data instances and unsupervised (KNN,CNN) mach...
International Journal of Bio-Science and Bio-Technology
Ameliorated MLP based Approach for Identification of Lung Diseases2016 •
The Journal of Allergy and Clinical Immunology: In Practice
Artificial Intelligence/Machine Learning in Respiratory Medicine and Potential Role in Asthma and COPD Diagnosis2021 •
2021 •
Preventing exacerbation and seeking to determine the severity of the disease during the hospitalization of chronic obstructive pulmonary disease (COPD) patients is a crucial global initiative for chronic obstructive lung disease (GOLD); this option is available only for stable-phase patients. Recently, the assessment and prediction techniques that are used have been determined to be inadequate for acute exacerbation of chronic obstructive pulmonary disease patients. To magnify the monitoring and treatment of acute exacerbation COPD patients, we need to rely on the AI system, because traditional methods take a long time for the prognosis of the disease. Machine-learning techniques have shown the capacity to be effectively used in crucial healthcare applications. In this paper, we propose a voting ensemble classifier with 24 features to identify the severity of chronic obstructive pulmonary disease patients. In our study, we applied five machine-learning classifiers, namely random forests (RF), support vector machine (SVM), gradient boosting machine (GBM), XGboost (XGB), and K-nearest neighbor (KNN). These classifiers were trained with a set of 24 features. After that, we combined their results with a soft voting ensemble (SVE) method. Consequently, we found performance measures with an accuracy of 91.0849%, a precision of 90.7725%, a recall of 91.3607%, an F-measure of 91.0656%, and an AUC score of 96.8656%, respectively. Our result shows that the SVE classifier with the proposed twenty-four features outperformed regular machine learning- based methods for chronic obstructive pulmonary disease (COPD) patients. The SVE classifier helps respiratory physicians to estimate the severity of COPD patients in the early stage, consequently guiding the cure strategy and helps the prognosis of COPD patients.
Cahiers Numismatiques,61, n° 239, 2024, p. 39-40
Les monnaies de bronze à légende EPVR provenant de Montlaurès (Narbonne,(Aude),2024 •
Fieldwork in Religion, Vol. 17, No. 2, 2022, pp. 244-245.
Anna Lutkajtis (University of Sydney), Review of Steven J. Sutcliffe and Carole M. Cusack (eds), The Problem of Invented ReligionsJurnal Cakrawala Mandarin
针对初级汉语学习者的汉字书写偏误 Analisis Kesalahan Penulisan Aksara Mandarin Pada Pembelajar Bahasa Mandarin Pemula2022 •
Behavioural Neurology
Association between Scale-Free Brain Dynamics and Behavioral Performance: Functional MRI Study in Resting State and Face Processing Task2017 •
Modern Medicine
The Economic Value of Job Crafting Interventions in Healthcare: An Utility Analysis Based on Romanian Income Data2023 •
2014 •
En, desde y hacia las américas . Músicas y migraciones transoceánicas
Musique Concrète Instrumentale and Coloniality of Knowledge2021 •
Revista Ciências Humanas
Panorama Das Pesquisas Sobre Acessibilidade e Lazer De Idosos Em Parques e Áreas NaturaisIntechOpen eBooks
Alveolar Ridge Augmentation Techniques in Implant Dentistry2021 •
International Journal of Civil Engineering and Architecture Engineering
Maximizing project efficiency through construction prefabrication unveiling the benefits2023 •
Research, Society and Development
Neurociência e educação: um mapeamento sobre influências, conexões e desafios para o ensino-aprendizagem2022 •