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    Parallel CNN-ELM: A Multiclass Classification of Chest X-Ray Images to Identify Seventeen Lung Diseases Including COVID-19

    Nahiduzzaman, Md, Omaer Faruq Goni, Md, Hassan, Rakibul, Robiul Islam, Md, Khalid Syfullah, Md, Mohammed Shahriar, Saleh, Shamim Anower, Md, Ahsan, Mominul, Haider, Julfikar ORCID logoORCID: https://orcid.org/0000-0001-7010-8285 and Kowalski, Marcin (2023) Parallel CNN-ELM: A Multiclass Classification of Chest X-Ray Images to Identify Seventeen Lung Diseases Including COVID-19. Expert Systems with Applications, 229 (A). 120528. ISSN 0957-4174

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    Abstract

    Numerous epidemic lung diseases such as COVID-19, tuberculosis (TB), and pneumonia have spread over the world, killing millions of people. Medical specialists have experienced challenges in correctly identifying these diseases due to their subtle differences in Chest X-ray images (CXR). To assist the medical experts, this study proposed a computer-aided lung illness identification method based on the CXR images. For the first time, 17 different forms of lung disorders were considered and the study was divided into six trials with each containing two, two, three, four, fourteen, and seventeen different forms of lung disorders. The proposed framework combined robust feature extraction capabilities of a lightweight parallel convolutional neural network (CNN) with the classification abilities of the extreme learning machine algorithm named CNN-ELM. An optimistic accuracy of 90.92% and an area under the curve (AUC) of 96.93% was achieved when 17 classes were classified side by side. It also accurately identified COVID-19 and TB with 99.37% and 99.98% accuracy, respectively, in 0.996 microseconds for a single image. Additionally, the current results also demonstrated that the framework could outperform the existing state-of-the-art (SOTA) models. On top of that, a secondary conclusion drawn from this study was that the prospective framework retained its effectiveness over a range of real-world environments, including balanced-unbalanced or large-small datasets, large multiclass or simple binary class, and high- or low-resolution images. A prototype Android App was also developed to establish the potential of the framework in real-life implementation.

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