ResNet-101 Empowered Deep Learning for Breast Cancer Ultrasound Image Classification

Agnesh Yadav, Maheshkumar Kolekar, Mukesh Zope

2024

Abstract

In the modern era, accurate breast cancer classification plays a crucial role in early detection and treatment planning. This article introduces a modified ResNet-101 architecture tailored specifically for classifying breast cancer using ultrasound images. The ultrasound images undergo pre-processing before passing through our adapted ResNet-101 model, which includes the integration of shortcut connections to enhance gradient stability and deep structure adaptability for effective learning and classification. The dataset comprises 780 images categorized into normal, benign, and malignant cases. To address class imbalance, data augmentation techniques are employed, enriching diversity and enhancing modeling precision. The proposed model achieves exceptional performance, boasting precision, recall, F1-score, and accuracy values of 0.9855, 0.9677, 0.9756, and 0.9743, respectively. The comparative analysis highlights the superiority of our model over existing techniques. Furthermore, we explore its potential for clinical application using real-world datasets. Our findings indicate significant promise in revolutionizing breast cancer detection, offering a robust tool for early and accurate diagnosis with the potential to impact patient outcomes greatly.

Paper Citation

in Harvard Style

Yadav A., Kolekar M. and Zope M. (2024). ResNet-101 Empowered Deep Learning for Breast Cancer Ultrasound Image Classification. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS; ISBN 978-989-758-688-0, SciTePress, pages 763-769. DOI: 10.5220/0012377800003657

in Bibtex Style

@conference{biosignals24,
author={Agnesh Yadav and Maheshkumar Kolekar and Mukesh Zope},
title={ResNet-101 Empowered Deep Learning for Breast Cancer Ultrasound Image Classification},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS},
year={2024},
pages={763-769},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012377800003657},
isbn={978-989-758-688-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS
TI - ResNet-101 Empowered Deep Learning for Breast Cancer Ultrasound Image Classification
SN - 978-989-758-688-0
AU - Yadav A.
AU - Kolekar M.
AU - Zope M.
PY - 2024
SP - 763
EP - 769
DO - 10.5220/0012377800003657
PB - SciTePress