Breast Cancer Diagnosis Using Bagging Decision Trees with Improved Feature Selection-Reference-Cited by-同舟云学术

Breast Cancer Diagnosis Using Bagging Decision Trees with Improved Feature Selection

Published:2023-12-11 Issue: Volume: Page:
ISSN:
Container-title:RAiSE-2023
language:
Short-container-title:

Author:

Dudeja Deepak¹^ORCID,Noonia Ajit²^ORCID,Lavanya S.³,Sharma Vandana⁴,Kumar Varun⁵,Rehan Sumaiya⁶,Ramkumar R.⁷

Affiliation:

1. Department of Computer Science and Engineering, Maharishi Markandeshwar Deemed to be University, Ambala 133203, India

2. Department of Computer Science and Engineering, Manipal University Jaipur, Jaipur 302034, India

3. Department of Computer Science and Engineering, R.V.S. College of Engineering, RVS Nagar, Dindigul 624005, India

4. Department of Computer Science, ABES Engineering College, Ghaziabad 201009, India

5. Department of Mathematics, School of Arts and Sciences, University of the People, Pasadena, CA 00012, USA

6. Department of Computer Science and Engineering, BBD University, Lucknow 226010, India

7. Department of Electrical and Electronics Engineering, School of Engineering and Technology, Dhanalakshmi Srinivasan University, Samayapuram 621112, India

Publisher

MDPI

Link

https://www.mdpi.com/2673-4591/59/1/17/pdf

Reference22 articles.

1. Automatic Classification Breast Masses in Mammograms using Fusion Technique and FLDA Analysis;Rao;Int. J. Innov. Technol. Explor. Eng.,2019

2. Angulo, P.A., Castellano, C.R., Rodriguez, C.A., and González, M.J.L. (2019). Value of a computer-assisted detection (CAD) system designed for digital mammography (DM) in the diagnosis of breast cancer assessed by DM and digital breast tomosynthesis (DBT). Eur. Congr. Radiol., 1–49.

3. Automatic mass detection in mammograms using deep convolutional neural networks;Agarwal;J. Med. Imaging,2019

4. Application of artificial intelligence-based classifiers to predict the outcome measures and stone-free status following percutaneous nephrolithotomy for staghorn calculi: Cross-validation of data and estimation of accuracy;Hameed;J. Endourol.,2021

5. An overview of deep learning in medical imaging focusing on MRI;Lundervold;Z. Für Med. Phys.,2019