Advanced deep learning and transfer learning approaches for breast cancer classification using advanced multi-line classifiers and datasets with model optimization and interpretability

Advanced deep learning and transfer learning approaches for breast cancer classification using advanced multi-line classifiers and datasets with model optimization and interpretability

This study evaluated machine learning (ML) models on the Wisconsin Breast Cancer Dataset (WBCD), refined to 554 unique instances after addressing 5% missing values via mean imputation, removing 15 duplicates, and normalizing features with Min–Max scaling. Data were split into 80% training and 20% te...

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Bibliographic Details
Main Authors: Xiang Zhang, Wei Shao, Ming Qiu, Chenglin Xiao, Liming Ma
Format: Article
Language:English
Published: PeerJ Inc. 2025-07-01
Series:PeerJ Computer Science
Subjects:
Breast cancer diagnosis
Machine learning
Supervised learning
Random forest
XGBoost
Deep neural networks (DNN)
Online Access:https://peerj.com/articles/cs-2951.pdf
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