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

Vivek Lahoura¹, Harpreet Singh¹, Ashutosh Aggarwal²

¹Department of Computer Science and Engineering, DAV University, Jalandhar 144 012, Punjab, India.

Diagnostics (Basel, Switzerland)

|February 9, 2021

Summary

This study introduces a cloud-based Extreme Learning Machine (ELM) model for early breast cancer detection. The machine learning approach significantly improves remote diagnostic accuracy, aiding women in underserved areas.

Keywords:

breast cancer cloud computing extreme learning machine telehealth

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Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare
Computational Biology

Background:

Breast cancer remains a leading cause of mortality in women globally.
Early detection and prompt treatment are crucial for reducing breast cancer deaths.
Machine learning and cloud computing offer solutions for remote diagnostics, especially in areas with limited medical facilities.

Purpose of the Study:

To develop and evaluate a cloud-based Extreme Learning Machine (ELM) framework for accurate breast cancer diagnosis.
To integrate feature selection methods for enhanced diagnostic performance.
To compare the proposed ELM system with existing state-of-the-art diagnostic technologies.

Main Methods:

Application of Extreme Learning Machine (ELM), a variant of Artificial Neural Networks (ANN), for breast cancer classification.
Utilizing the gain ratio feature selection method to identify and remove insignificant features.
Developing a cloud computing-based system to facilitate remote breast cancer diagnosis.

Main Results:

The proposed cloud-based ELM model achieved high performance on the Wisconsin Diagnostic Breast Cancer (WBCD) dataset.
Achieved an accuracy of 0.9868, recall of 0.9130, precision of 0.9054, and F1-score of 0.8129.
The ELM technique demonstrated superior performance compared to other state-of-the-art diagnostic methods in both standalone and cloud environments.

Conclusions:

The developed cloud-based ELM system shows significant promise for accurate and accessible remote breast cancer diagnosis.
The integration of ELM and feature selection enhances diagnostic capabilities, particularly for telehealth services.
This approach can aid radiologists and improve patient outcomes, especially in remote or underserved populations.