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

• Main Authors: Vivek Lahoura, Harpreet Singh, Ashutosh Aggarwal, Bhisham Sharma, Mazin Abed Mohammed, Robertas Damaševičius, Seifedine Kadry, Korhan Cengiz
• Format: Article
• Language: English
• Published: MDPI AG 2021-02-01
• Series: Diagnostics
• Subjects: breast cancer; extreme learning machine; cloud computing; telehealth
• Online Access: https://www.mdpi.com/2075-4418/11/2/241

Globally<b>, </b>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.