Toward Improving Breast Cancer Classification Using an Adaptive Voting Ensemble Learning Algorithm-Reference-Cited by-同舟云学术

Toward Improving Breast Cancer Classification Using an Adaptive Voting Ensemble Learning Algorithm

Published:2024 Issue: Volume:12 Page:12869-12882
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Batool Amreen¹^ORCID,Byun Yung-Cheol²^ORCID

Affiliation:

1. Department of Electronic Engineering, Institute of Information Science and Technology, Jeju National University, Jeju-si, South Korea

2. Department of Computer Engineering, Major of Electronic Engineering, Institute of Information Science and Technology, Jeju National University, Jeju-si, South Korea

Funder

Ministry of Small and Medium-Sized Enterprises (SMEs) and Startups (MSS), South Korea, under the Regional Specialized Industry Development Plus Program

Korea Institute for Advancement of Technology

KIAT funded by the Korean Government [Ministry Of Trade, Industry & Energy (MOTIE)]

Regional Innovation Strategy (RIS) through the National Research Foundation of Korea (NRF) funded by the Ministry of Education

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10410858.pdf?arnumber=10410858

Reference61 articles.

1. Breast Cancer Statistics, 2022

2. A cross-cohort computational framework to trace tumor tissue-of-origin based on RNA sequencing

3. Semi-supervised vision transformer with adaptive token sampling for breast cancer classification

4. Overexpression of SOCS4 inhibits proliferation and migration of cervical cancer cells by regulating JAK1/STAT3 signaling pathway

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