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

G Siva Shankar; Edeh Michael Onyema; Balasubramanian Prabhu Kavin; Venkataramaiah Gude; Bvv Siva Prasad

doi:10.1177/11795972241278907

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

Biomed Eng Comput Biol. 2024 Oct 28:15:11795972241278907. doi: 10.1177/11795972241278907. eCollection 2024.

Authors

G Siva Shankar¹, Edeh Michael Onyema^{2

3}, Balasubramanian Prabhu Kavin⁴, Venkataramaiah Gude⁵, Bvv Siva Prasad⁶

Affiliations

¹ Department of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur Campus, Chengalpattu, Tamil Nadu, India.
² Department of Mathematics and Computer Science, Coal City University Nigeria, Enugu, Nigeria.
³ Adjunct Faculty, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India.
⁴ Department of Data Science and Business System, SRM Institute of Science and Technology, Kattankulathur Campus, Chengalpattu, Tamil Nadu, India.
⁵ Software Engineer in GP Technologies LLC, Texas, USA.
⁶ Department of Computer Science and Engineering, School of Engineering (CSE), Anurag University, Hyderabad, Telangana, India.

Abstract

One of the leading causes of death for women worldwide is breast cancer. Early detection and prompt treatment can reduce the risk of breast cancer-related death. Cloud computing and machine learning are crucial for disease diagnosis today, but they are especially important for those who live in distant places with poor access to healthcare. While machine learning-based diagnosis tools act as primary readers and aid radiologists in correctly diagnosing diseases, cloud-based technology can also assist remote diagnostics and telemedicine services. The promise of techniques based on Artificial Neural Networks (ANN) for sickness diagnosis has attracted the attention of several re-searchers. The 4 methods for the proposed research include preprocessing, feature extraction, and classification. A Smart Window Vestige Deletion (SWVD) technique is initially suggested for preprocessing. It consists of Savitzky-Golay (S-G) smoothing, updated 2-stage filtering, and adaptive time window division. This technique separates each channel into multiple time periods by adaptively pre-analyzing its specificity. On each window, an altered 2-stage filtering process is then used to retrieve some tumor information. After applying S-G smoothing and integrating the broken time sequences, the process is complete. In order to deliver effective feature extraction, the Deep Residual based Multiclass for architecture (DRMFA) is used. In histological photos, identify characteristics at the cellular and tissue levels in both tiny and large size patches. Finally, a fresh customized strategy that combines a better crow forage-ELM. Deep learning and the Extreme Learning Machine (ELM) are concepts that have been developed (ACF-ELM). When it comes to diagnosing ailments, the cloud-based ELM performs similarly to certain cutting-edge technology. The cloud-based ELM approach beats alternative solutions, according to the DDSM and INbreast dataset results. Significant experimental results show that the accuracy for data inputs is 0.9845, the precision is 0.96, the recall is 0.94, and the F1 score is 0.95.

Keywords: Remote diagnostics; ameliorate crow forage-ELM; deep residual based multiclass for feature extraction approach; smart window vestige deletion.