Extreme Learning Machine for Cloud-Based Breast Cancer Diagnosis
Ashwini V. Malviya, Raman Batra, Ms.Vyshnavi A · 2024
Breast cancer is one of the leading causes of death for women worldwide, which emphasises how important it is to discover the disease early and treat it quickly to reduce the dangers involved. Machine learning combined with cloud computing has become a major player in the field of illness diagnosis, especially in areas where access to healthcare facilities is scarce. This novel method functions as a supplementary reader to improve the precision of illness diagnosis, providing assistance to radiologists. Furthermore, cloud-based solutions ease the difficulties experienced by those living in distant locations by enabling telehealth offerings and remote diagnostics. The potential of neural network training (ANN), a widely used machine learning approach, in the detection of illness has attracted interest. Lastly, a method based on cloud computing is suggested to diagnose breast cancer remotely via ELM. When compared to cutting-edge technology for disease detection, the suggested cloud-based ELM approach performs better; this was especially confirmed using the Wisconsin Diagnosed Breast Cancer Diagnostics (WBCD) dataset. Analytical comparisons show how the cloud-based ELM performs better than other approaches, proving its effectiveness in improving diagnostic precision. This research establishes ELM as a reliable option for breast cancer detection by highlighting its capabilities in standalone and cloud settings.