Enhancing breast cancer detection from histopathology images: A novel ensemble approach with deep learning-based feature extraction

¹ Computer Science and Engineering, Madanapalle Institute of Technology & Science, Kadiri Road, Angallu (V), Madanapalle - 517325, Annamayya District, Andhra Pradesh, India
² Department of Computer Science and Engineering, Institute of Aeronautical Engineering, Hyderabad
³ Department of Engineering and Technology, Botho University, Botswana
⁴ Department of CSE, Rajiv Gandhi University of Knowledge Technologies, IIIT Nuzvid
⁵ Department of Computer Science and Engineering, R.V.R. & J.C. College of Engineering (Autonomous), Chowdavaram, Guntur - 522019, Andhra Pradesh
⁶ Department of Computer Science and Engineering, Malla Reddy Engineering College for Women, Maisammaguda, Dhulapally, Secunderabad
⁷ Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur District, Andhra Pradesh - 522302, India

^* Corresponding author: drsundarr@mits.ac.in

Abstract

Effective detection and diagnostic procedures are necessary to enhance patient results for the common and life-threatening illness of breast cancer. Current approaches have limits in scalability and efficiency, highlighting the need for more study. This work introduces a hybrid Breast Cancer (BC) detecting approach that merges Deep Learning (DL) with pre-trained modeling of Histopathology Images (HPI) and an ensemble-based Machine Learning (ML) approach. DL integration allows learning and identifying hidden trends in intricate BC pictures, while ML techniques provide interpretability and generalization skills. Contrast Limited Adaptive Histogram Equalization (CLAHE) was used on HPI as a pre-processing technique to improve picture quality. The ResNet50V2 model was used for deep feature extraction. The Ensemble Learning (EL) model combines predictions from four basic ML approaches using soft voting. The research attained a superior accuracy, precision, recall, and F1 score compared to the most advanced models. This study provides substantial advancements in breast cancer diagnosis, thorough performance evaluation, and reliable assessment. Furthermore, it helps medical personnel make well-informed choices, enhance patient care, and improve results for BC sufferers.