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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 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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Multi spectral classification and recognition of breast cancer and pneumonia

Kakde Aditya, Arora Nitin, Sharma Durgansh, Sharma Subhash Chander

Polish Journal of Medical Physics and Engineering

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2020

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Vol. 26, Iss. 1

1--9

EN

According to the Google I/O 2018 key notes, in future artificial intelligence, which also includes machine learning and deep learning, will mostly evolve in healthcare domain. As there are lots of subdomains which come under the category of healthcare domain, the proposed paper concentrates on one such domain, that is breast cancer and pneumonia. Today, just classifying the diseases is not enough. The system should also be able to classify a particular patient’s disease. Thus, this paper shines the light on the importance of multi spectral classification which means the collection of several monochrome images of the same scene. It can be proved to be an important process in the healthcare areas to know if a patient is suffering from a specific disease or not. The convolutional layer followed by the pooling layer is used for the feature extraction process and for the classification process; fully connected layers followed by the regression layer are used.

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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.