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Open AccessArticle

Cloud Computing-Based Framework for Breast Cancer Diagnosis Using Extreme Learning Machine

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Department of Computer Science and Engineering, DAV University, Jalandhar 144 012, Punjab, India
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Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala 147004, Punjab, India
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Chitkara University School of Engineering and Technology, Chitkara University, Himachal Pradesh, India
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Information Systems Department, College of Computer Science and Information Technology, University of Anbar, 55431 Ramadi, Anbar, Iraq
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Department of Applied Informatics, Vytautas Magnus University, 44404 Kaunas, Lithuania
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Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland
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Faculty of Applied Computing and Technology (FACT), Noroff University College, 4608 Kristiansand, Norway
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Department of Electrical—Electronics Engineering, Trakya University, Edirne 22030, Turkey
*
Author to whom correspondence should be addressed.
Academic Editor: Ryad Zemouri
Diagnostics 2021, 11(2), 241; https://doi.org/10.3390/diagnostics11020241
Received: 6 December 2020 / Revised: 28 January 2021 / Accepted: 29 January 2021 / Published: 4 February 2021
(This article belongs to the Special Issue Machine Learning in Breast Cancer Diagnosis and Prognosis)
Globally, breast cancer is one of the most significant causes of death among women. Early detection accompanied by prompt treatment can reduce the risk of death due to breast cancer. Currently, machine learning in cloud computing plays a pivotal role in disease diagnosis, but predominantly among the people living in remote areas where medical facilities are scarce. Diagnosis systems based on machine learning act as secondary readers and assist radiologists in the proper diagnosis of diseases, whereas cloud-based systems can support telehealth services and remote diagnostics. Techniques based on artificial neural networks (ANN) have attracted many researchers to explore their capability for disease diagnosis. Extreme learning machine (ELM) is one of the variants of ANN that has a huge potential for solving various classification problems. The framework proposed in this paper amalgamates three research domains: Firstly, ELM is applied for the diagnosis of breast cancer. Secondly, to eliminate insignificant features, the gain ratio feature selection method is employed. Lastly, a cloud computing-based system for remote diagnosis of breast cancer using ELM is proposed. The performance of the cloud-based ELM is compared with some state-of-the-art technologies for disease diagnosis. The results achieved on the Wisconsin Diagnostic Breast Cancer (WBCD) dataset indicate that the cloud-based ELM technique outperforms other results. The best performance results of ELM were found for both the standalone and cloud environments, which were compared. The important findings of the experimental results indicate that the accuracy achieved is 0.9868, the recall is 0.9130, the precision is 0.9054, and the F1-score is 0.8129. View Full-Text
Keywords: breast cancer; extreme learning machine; cloud computing; telehealth breast cancer; extreme learning machine; cloud computing; telehealth
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MDPI and ACS Style

Lahoura, V.; Singh, H.; Aggarwal, A.; Sharma, B.; Mohammed, M.A.; Damaševičius, R.; Kadry, S.; Cengiz, K. Cloud Computing-Based Framework for Breast Cancer Diagnosis Using Extreme Learning Machine. Diagnostics 2021, 11, 241. https://doi.org/10.3390/diagnostics11020241

AMA Style

Lahoura V, Singh H, Aggarwal A, Sharma B, Mohammed MA, Damaševičius R, Kadry S, Cengiz K. Cloud Computing-Based Framework for Breast Cancer Diagnosis Using Extreme Learning Machine. Diagnostics. 2021; 11(2):241. https://doi.org/10.3390/diagnostics11020241

Chicago/Turabian Style

Lahoura, Vivek; Singh, Harpreet; Aggarwal, Ashutosh; Sharma, Bhisham; Mohammed, Mazin A.; Damaševičius, Robertas; Kadry, Seifedine; Cengiz, Korhan. 2021. "Cloud Computing-Based Framework for Breast Cancer Diagnosis Using Extreme Learning Machine" Diagnostics 11, no. 2: 241. https://doi.org/10.3390/diagnostics11020241

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