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Detection of various lung diseases including COVID-19 using extreme learning machine algorithm based on the features extracted from a lightweight CNN architecture

Nahiduzzaman Md., Faruq Goni Md Omaer, Robiul Islam Md., Sayeed Abu, Shamim Anower Md., Ahsan Mominul, Haider Julfikar, Kowalski Marcin

Biocybernetics and Biomedical Engineering

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2023

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Vol. 43, no. 3

528--550

EN

Around the world, several lung diseases such as pneumonia, cardiomegaly, and tuberculosis (TB) contribute to severe illness, hospitalization or even death, particularly for elderly and medically vulnerable patients. In the last few decades, several new types of lungrelated diseases have taken the lives of millions of people, and COVID-19 has taken almost 6.27 million lives. To fight against lung diseases, timely and correct diagnosis with appropriate treatment is crucial in the current COVID-19 pandemic. In this study, an intelligent recognition system for seven lung diseases has been proposed based on machine learning (ML) techniques to aid the medical experts. Chest X-ray (CXR) images of lung diseases were collected from several publicly available databases. A lightweight convolutional neural network (CNN) has been used to extract characteristic features from the raw pixel values of the CXR images. The best feature subset has been identified using the Pearson Correlation Coefficient (PCC). Finally, the extreme learning machine (ELM) has been used to perform the classification task to assist faster learning and reduced computational complexity. The proposed CNN-PCC-ELM model achieved an accuracy of 96.22% with an Area Under Curve (AUC) of 99.48% for eight class classification. The outcomes from the proposed model demonstrated better performance than the existing state-of-the-art (SOTA) models in the case of COVID-19, pneumonia, and tuberculosis detection in both binary and multiclass classifications. For eight class classification, the proposed model achieved precision, recall and fi-score and ROC are 100%, 99%, 100% and 99.99% respectively for COVID-19 detection demonstrating its robustness. Therefore, the proposed model has overshadowed the existing pioneering models to accurately differentiate COVID-19 from the other lung diseases that can assist the medical physicians in treating the patient effectively.

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A novel deep learning-based approach for prediction of neonatal respiratory disorders from chest X-ray images

Erdogan Yildirim Ayse, Canayaz Murat

Biocybernetics and Biomedical Engineering

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2023

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Vol. 43, no. 4

635--655

EN

In recent years, many diseases can be diagnosed in a short time with the use of deep learning models in the field of medicine. Most of the studies in this area focus on adult or pediatric patients. However, deep learning studies for the diagnosis of diseases in neonatal are not sufficient. Also, since it is known that respiratory disorders such as pneumonia have a large place among the causes of neonatal death, early and accurate diagnosis of respiratory diseases in neonates is crucial. For this reason, our study aims to detect the presence of respiratory disorders through the developed deep-learning approach using chest X-ray images of patients hospitalized in the Neonatal Intensive Care Unit. Accordingly, the enhanced version of C+EffxNet, the new hybrid deep learning model, is designed to predict respiratory disorders in neonates. In this version, the features selected by PCA are combined as 100, 200, and 300, then the binary classification process was carried out. In the study, the accuracy and kappa value were obtained as 0.965, and 0.904, respectively before feature merging, while these values were obtained as 0.977, and 0.935 after feature merging. This method, which was developed for the diagnosis of respiratory disorders in neonates, was also subsequently applied to a chest X-ray dataset that is frequently used in the literature for the diagnosis of pediatric pneumonia. For this data set, while the accuracy was 0.992, the kappa value was 0.982. The results obtained confirm the success of the proposed method for both datasets.

3

Respiratory sound denoising using sparsity-assisted signal smoothing algorithm

Haider Nishi Shahnaj, Behera Ajoy K.

Biocybernetics and Biomedical Engineering

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2022

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Vol. 42, no. 2

481--493

EN

Noises are the unavoidable entities which stands as a big barrier in the field of computerized lung sound (LS) based disease diagnosis, as it impairs the quality of LS and therefore greatly misleads the clinical interpretations done based on that. It has numerous sources, of which a few of them could be the noises due to body sounds, environmental noises, power line noises and recording artifacts, which easily contaminates the LS recordings. This paper presents a novel denoising algorithm to eliminate the noises from LS recordings in a more powerful way using Butterworth band-pass filter and sparsity assisted signal smoothing (SASS) algorithm. This study is carried out over LS captured from 80 Chronic Obstructive Pulmonary Disease (COPD), 80 pneumonia and 80 healthy participants in a clinical environment. Each of the recorded LS is denoised using Butterworth band-pass filter and sparse-assisted signal smoothing algorithm. The denoising performance of the proposed algorithm is evaluated on the basis of denoising performance parameters. As per the evaluation of the denoising performance parameters, it is observed that the proposed denoising method suppressed the LS noises with the signal to noise ratio (SNR) of 66.8 dB and with the peak signal to noise ratio (PSNR) of 78.5 dB. The proposed endeavour can be recommended for clinical use for producing noise free LSs to bring effective interpretations. The future endeavours involve the suppression of the heart sound noises from the LS recordings.

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Detection of pneumonia using convolutional neural networks and deep learning

Szepesi Patrik, Szilágyi László

Biocybernetics and Biomedical Engineering

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2022

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Vol. 42, no. 3

1012--1022

EN

The objective and automated detection of pneumonia represents a serious challenge in medical imaging, because the signs of the illness are not obvious in CT or X-ray scans. Further on, it is also an important task, since millions of people die of pneumonia every year. The main goal of this paper is to propose a solution for the above mentioned problem, using a novel deep neural network architecture. The proposed novelty consists in the use of dropout in the convolutional part of the network. The proposed method was trained and tested on a set of 5856 labeled images available at one of Kaggle’s many medical imaging challenges. The chest X-ray images (anterior-posterior) were selected from retrospective cohorts of pediatric patients, aged between one and five years, from Guangzhou Women and Children’s Medical Center, Guangzhou, China. Results achieved by our network would have placed first in the Kaggle competition with the following metrics: 97.2% accuracy, 97.3% recall, 97.4% precision and AUC ₌ 0:982, and they are competitive with current state-of-the-art solutions.

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A machine learning model for supporting symptom-based referral and diagnosis of bronchitis and pneumonia in limited resource settings

Stokes Katy, Castaldo Rossana, Franzese Monica, Salvatore Marco, Fico Giuseppe, Pokvic Lejla Gurbeta, Badnjevic Almir, Pecchia Leandro

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 4

1288--1302

EN

Pneumonia is a leading cause of mortality in limited resource settings (LRS), which are common in low- and middle-income countries (LMICs). Accurate referrals can reduce the devastating impact of pneumonia, especially in LRS. Discriminating pneumonia from other respiratory conditions based only on symptoms is a major challenge. Machine learning has shown promise in overcoming the diagnostic difficulties of pneumonia (i.e., low specificity of symptoms, lack of accessible diagnostic tests and varied clinical presentation). Many scientific papers are now focusing on deep-learning methods applied to clinical images, which is unaffordable for initial patient referral in LMICs. The current study used a dataset of 4500 patients (1500 patients affected by bronchitis, 3000 by pneumonia) from a middle-income country, containing information on subject population characteristics, symptoms and laboratory test results. Manual feature selection was performed, focusing on clinical symptoms that are easily measurable in LRS and in community settings. Three common machine learning methods were tested and compared: logistic regression; decision tree and support vector machine. Models were developed through a holdout process of training-validation and testing. We focused on six clinically relevant, easily interpreted patient symptoms as best indicators for pneumonia. Our final model was a decision tree, achieving an AUC of 93%, with the advantage of being fully intelligible and easily interpreted. The performance achieved suggested that intelligible machine learning models can enhance symptom-based referral of pneumonia in LRS and in community settings.

6

A deep learning-based COVID-19 automatic diagnostic framework using chest X-ray images

Joshi Rakesh Chandra, Yadav Saumya, Pathak Vinay Kumar, Malhotra Hardeep Singh, Khokhar Harsh Vardhan Singh, Parihar Anit, Kohli Neera, Himanshu D., Garg Ravindra K., Bhatt Madan Lal Brahma, Kumar Raj, Singh Naresh Pal, Sardana Vijay, Burget Radim, Alippi Cesare, Travieso-Gonzalez Carlos M., Dutta Malay Kishore

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 1

239--254

EN

The lethal novel coronavirus disease 2019 (COVID-19) pandemic is affecting the health of the global population severely, and a huge number of people may have to be screened in the future. There is a need for effective and reliable systems that perform automatic detection and mass screening of COVID-19 as a quick alternative diagnostic option to control its spread. A robust deep learning-based system is proposed to detect the COVID-19 using chest X-ray images. Infected patient's chest X-ray images reveal numerous opacities (denser, confluent, and more profuse) in comparison to healthy lungs images which are used by a deep learning algorithm to generate a model to facilitate an accurate diagnostics for multi-class classification (COVID vs. normal vs. bacterial pneumonia vs. viral pneumonia) and binary classification (COVID-19 vs. non-COVID). COVID-19 positive images have been used for training and model performance assessment from several hospitals of India and also from countries like Australia, Belgium, Canada, China, Egypt, Germany, Iran, Israel, Italy, Korea, Spain, Taiwan, USA, and Vietnam. The data were divided into training, validation and test sets. The average test accuracy of 97.11 ± 2.71% was achieved for multi-class (COVID vs. normal vs. pneumonia) and 99.81% for binary classification (COVID-19 vs. non-COVID). The proposed model performs rapid disease detection in 0.137 s per image in a system equipped with a GPU and can reduce the workload of radiologists by classifying thousands of images on a single click to generate a probabilistic report in real-time.

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A deep learning approach to detect Covid-19 coronavirus with X-Ray images

Jain Govardhan, Mittal Deepti, Thakur Daksh, Mittal Madhup K.

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 4

1391--1405

EN

Rapid and accurate detection of COVID-19 coronavirus is necessity of time to prevent and control of this pandemic by timely quarantine and medical treatment in absence of any vaccine. Daily increase in cases of COVID-19 patients worldwide and limited number of available detection kits pose difficulty in identifying the presence of disease. Therefore, at this point of time, necessity arises to look for other alternatives. Among already existing, widely available and low-cost resources, X-ray is frequently used imaging modality and on the other hand, deep learning techniques have achieved state-of-the-art performances in computer-aided medical diagnosis. Therefore, an alternative diagnostic tool to detect COVID-19 cases utilizing available resources and advanced deep learning techniques is proposed in this work. The proposed method is implemented in four phases, viz., data augmentation, preprocessing, stage-I and stage-II deep network model designing. This study is performed with online available resources of 1215 images and further strengthen by utilizing data augmentation techniques to provide better generalization of the model and to prevent the model overfitting by increasing the overall length of dataset to 1832 images. Deep network implementation in two stages is designed to differentiate COVID-19 induced pneumonia from healthy cases, bacterial and other virus induced pneumonia on X-ray images of chest. Comprehensive evaluations have been performed to demonstrate the effectiveness of the proposed method with both (i) training-validation-testing and (ii) 5- fold cross validation procedures. High classification accuracy as 97.77%, recall as 97.14% and precision as 97.14% in case of COVID-19 detection shows the efficacy of proposed method in present need of time. Further, the deep network architecture showing averaged accuracy/ sensitivity/specificity/precision/F1-score of 98.93/98.93/98.66/96.39/98.15 with 5-fold cross validation makes a promising outcome in COVID-19 detection using X-ray images.

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Zagrożenie bakteriami z rodzaju Legionella w środowisku pracy

Stojek N. M.

Podstawy i Metody Oceny Środowiska Pracy

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2004

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Nr 3 (41)

61--67

PL

Bakterie z rodzaju Legionella są rozpowszechnione w środowisku, zwłaszcza wilgotnym. Mają zdolność kolonizowania sieci wodociągowej i różnorodnych zbiorników wodnych sztucznych i naturalnych. Dotychczas wyodrębniono 42 gatunki i 64 grupy serologiczne, z których najczęściej przyczyną zachorowań jest L. pneumophila, serotyp 1. Człowiek zaraża się przez drogi oddechowe, wdychając zakażone pyły lub wodne aerozole. Do szczególnie narażonych na zakażenia należą osoby przebywające w pomieszczeniach o wzmożonej wilgotności powietrza. Legioneloza przebiega pod postacią ciężkiego zapalenia płuc o śmiertelności około 20% lub gorączki Pontiac o znacznie łagodniejszym przebiegu, bez przypadków śmiertelnych.

EN

The bacteria of genus Legionella are commonly present in the environment, especially in humid conditions. To date 42 species have been identifid of which Legionella pneumophila is the most frequent cause of diseases (1). Infections take place through the airways by inhalation of droplet aerosols from water or dust. The risk of infection is increased by high humidity of the air. Legionellosis may occur as a pneumonia with mortality of about 20%, or as a much milder flu-like illness called Pontiac fever.