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

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

Identyfikatory

DOI

10.1016/j.bbe.2023.06.003

• Języki publikacji: EN

Abstrakty

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.

Słowa kluczowe

EN

COVID-19; convolutional neural network; extreme learning machine; Pearson correlation coefficient; pneumonia

PL

COVID-19; sieć neuronowa konwolucyjna; maszyna ucząca się; współczynnik korelacji Pearsona; zapalenie płuc

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2023
• Tom: Vol. 43, no. 3
• Strony: 528--550
• Opis fizyczny: Bibliogr. 90 poz., rys., tab., wykr.

Twórcy

autor

Nahiduzzaman Md.

• Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh

autor

Faruq Goni Md Omaer

• Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh

autor

Robiul Islam Md.

• Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh

autor

Sayeed Abu

• Department of Computer Science & Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh

autor

Shamim Anower Md.

• Department of Electrical & Electronic Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh

autor

Ahsan Mominul

• Department of Computer Science, University of York, Deramore Lane, Heslington, York YO10 5GH, UK

autor

Haider Julfikar

• Department of Engineering, Manchester Metropolitan University, Chester St, Manchester M1 5GD, UK

autor

Kowalski Marcin

• Institute of Optoelectronics, Military University of Technology, Gen. S. Kaliskiego 2, Warsaw, Poland

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