Improving Accuracy in Early Stage Breast Cancer Detection with a Dual Modality Segmentation Approach in Advancements for Breast Cancer Detection

DOI

10.2991/978-94-6463-740-3_3How to use a DOI?

Keywords

Breast cancer; Deep learning; Imaging modalities; Image processing; Machine learning

Abstract

Breast cancer is a commonly seen illness in women and is the primary reason for cancer-related deaths among women. The early detection of cancerous tissue will aid in recovery and treatment of it and can save more lives. The detection of breast cancer is challenging using mammography, as dense tissues can overlap in screening. In some cases, ultrasound and MRI have also been used for breast cancer screening. Many techniques have been used for the detection of anomalies in the breast. For breast cancer detection at the initial stage, many research works have been done. Machine learning is frequently used for detection. Currently, Deep learning, a modern method, is utilized for the categorization of cancer. Deep learning represents a more sophisticated type of machine learning. Convolutional neural networks and Recurrent neural networks are types of deep learning algorithms employed for image classification purposes. In this research, we have introduced an improved dual modality method for better image segmentation, which is essential for image classification, with algorithm for detecting early-stage breast cancer. The result of accurate segmentation using dual modality provides surety in the detection of the cancerous tissue is present or not.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Himanshu Patel
AU  - Anand Mankodia
PY  - 2025
DA  - 2025/06/25
TI  - Improving Accuracy in Early Stage Breast Cancer Detection with a Dual Modality Segmentation Approach in Advancements for Breast Cancer Detection
BT  - Proceedings of the 6th International Conference on Deep Learning, Artificial Intelligence and Robotics (ICDLAIR 2024)
PB  - Atlantis Press
SP  - 17
EP  - 30
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-740-3_3
DO  - 10.2991/978-94-6463-740-3_3
ID  - Patel2025
ER  -