Breast Cancer Diagnosis Using Virtualization and Extreme Learning Algorithm Based on Deep Feed Forward Networks

Area of Science:

• Medical Imaging and Diagnostics
• Artificial Intelligence in Healthcare
• Computational Biology

Background:

• Breast cancer is a leading cause of death for women globally, necessitating improved early detection and treatment strategies.
• Cloud computing and machine learning offer solutions for remote diagnostics and telemedicine, particularly in underserved areas.
• Artificial Neural Networks (ANNs) show promise for disease diagnosis, driving research into advanced computational methods.

Purpose of the Study:

• To develop and evaluate a novel cloud-based machine learning framework for accurate and efficient breast cancer diagnosis.
• To enhance early detection capabilities, thereby reducing breast cancer-related mortality.
• To leverage advanced AI techniques for improved diagnostic accuracy in remote healthcare settings.

Main Methods:

• A four-stage methodology involving preprocessing, feature extraction, and classification was employed.
• The Smart Window Vestige Deletion (SWVD) technique, incorporating Savitzky-Golay (S-G) smoothing and adaptive filtering, was used for preprocessing.
• Deep Residual based Multiclass for architecture (DRMFA) was utilized for feature extraction from histological images, followed by a custom crow forage-ELM (ACF-ELM) for classification.

Main Results:

• The proposed cloud-based Extreme Learning Machine (ELM) approach demonstrated performance comparable to state-of-the-art technologies.
• Evaluated on the DDSM and INbreast datasets, the ACF-ELM method outperformed alternative solutions.
• Achieved high performance metrics: 0.9845 accuracy, 0.96 precision, 0.94 recall, and 0.95 F1 score.

Conclusions:

• The developed cloud-based machine learning system offers a robust and accurate solution for breast cancer diagnosis.
• The novel SWVD and ACF-ELM techniques contribute to improved feature extraction and classification in medical imaging.
• This approach holds significant potential for enhancing telemedicine services and improving breast cancer outcomes, especially in remote areas.