A Novel Approach for Breast Cancer Detection Using a Modified Convolutional Neural Network

R. Srikanth, Dinesh A S, Prasanth S, Rengasamy B, Paul Rouso I

2025

Abstract

Breast cancer (BC) is a predominant cause of mortality globally. In 2020, over 10 million individuals worldwide succumbed to breast cancer. BC is a lethal disease and prevalent among women worldwide. It is classified fourth among the lethal malignancies, including colorectal cancer, cervical cancer, and brain tumors. In recent years, Convolutional Neural Networks (CNNs) have demonstrated exceptional efficacy in medical image categorization, especially in the identification of BC from mammographic pictures. Nevertheless, conventional CNN designs encounter constraints in feature extraction and detection precision. This research presents a Modified Convolutional Neural Network (MCNN) aimed at improving feature extraction and classification efficacy. The proposed MCNN incorporates architectural improvements, featuring optimized convolutional layers and an improved activation function, designed to maximize accuracy and minimize false positives. The model is trained and evaluated on a publicly accessible BC picture dataset, demonstrating substantial enhancements compared to conventional CNN designs. Critical performance indicators, including accuracy, precision, recall, and F1-score, illustrate the MCNN's exceptional categorization proficiency. The approach significantly decreases false positives, enhancing the reliability of diagnostic support in clinical settings. Visualizations of feature maps and heatmaps further emphasize the MCNN's capacity to detect significant areas in mammograms. The findings demonstrate that the proposed MCNN serves as an effective instrument for breast cancer detection, enhancing existing CNN-based models. The suggested model attains 99% accuracy, 98.7% precision, 97% recall, and 96.2% F1-score.

Paper Citation

in Harvard Style

Srikanth R., A S D., S P., B R. and Rouso I P. (2025). A Novel Approach for Breast Cancer Detection Using a Modified Convolutional Neural Network. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 781-788. DOI: 10.5220/0013664700004664

in Bibtex Style

@conference{incoft25,
author={R. Srikanth and Dinesh A S and Prasanth S and Rengasamy B and Paul Rouso I},
title={A Novel Approach for Breast Cancer Detection Using a Modified Convolutional Neural Network},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT},
year={2025},
pages={781-788},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013664700004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT
TI - A Novel Approach for Breast Cancer Detection Using a Modified Convolutional Neural Network
SN - 978-989-758-763-4
AU - Srikanth R.
AU - A S D.
AU - S P.
AU - B R.
AU - Rouso I P.
PY - 2025
SP - 781
EP - 788
DO - 10.5220/0013664700004664
PB - SciTePress