Intelligent breast cancer diagnosis with two-stage using mammogram images

被引：2

作者：

Muhammad Yaqub ^{[1
]}

Feng Jinchao ^{[2
]}

Nazish Aijaz ^{[1
]}

Shahzad Ahmed ^{[2
]}

Atif Mehmood ^{[3
]}

Hao Jiang ^{[4
]}

Lan He ^{[1
]}

机构：

[1] School of Biomedical Sciences, Hunan University, Changsha

[2] Faculty of Information Technology, Beijing University of Technology, Beijing

[3] Department of Computer Science and Technology, Zhejiang Normal University, Jinhua

[4] Department of Biomedical Informatics School of Life Sciences, Central South University, Hunan, Changsha

来源：

Scientific Reports | / 14卷 / 1期

基金：

中国国家自然科学基金;

关键词：

Atrous convolution based attentive and adaptive multi-scale DenseNet; Atrous convolution-based attentive and adaptive Trans-Res-UNet; Breast cancer; Mammograms; Modified mussel length-based eurasian oystercatcher optimization;

D O I：

10.1038/s41598-024-65926-0

中图分类号：

学科分类号：

摘要：

Breast cancer (BC) significantly contributes to cancer-related mortality in women, underscoring the criticality of early detection for optimal patient outcomes. Mammography is a key tool for identifying and diagnosing breast abnormalities; however, accurately distinguishing malignant mass lesions remains challenging. To address this issue, we propose a novel deep learning approach for BC screening utilizing mammography images. Our proposed model comprises three distinct stages: data collection from established benchmark sources, image segmentation employing an Atrous Convolution-based Attentive and Adaptive Trans-Res-UNet (ACA-ATRUNet) architecture, and BC identification via an Atrous Convolution-based Attentive and Adaptive Multi-scale DenseNet (ACA-AMDN) model. The hyperparameters within the ACA-ATRUNet and ACA-AMDN models are optimized using the Modified Mussel Length-based Eurasian Oystercatcher Optimization (MML-EOO) algorithm. The performance is evaluated using a variety of metrics, and a comparative analysis against conventional methods is presented. Our experimental results reveal that the proposed BC detection framework attains superior precision rates in early disease detection, demonstrating its potential to enhance mammography-based screening methodologies. © The Author(s) 2024.

引用

共 40 条

[1]

Wu N., Et al., Deep neural networks improve radiologists’ performance in breast cancer screening, IEEE Trans. Med. Imaging, 39, 4, pp. 1184-1194, (2019)

[2]

Sameti M., Et al., Image feature extraction in the last screening mammograms prior to detection of breast cancer, IEEE J. Sel. Top. Signal Process, 3, 1, pp. 46-52, (2009)

[3]

Geweid G.G., Abdallah M.A., A novel approach for breast cancer investigation and recognition using M-level set-based optimization functions, IEEE Access, 7, pp. 136343-136357, (2019)

[4]

Yaqub M., Et al., GAN-TL: Generative adversarial networks with transfer learning for MRI reconstruction, Appl. Sci, 12, 17, (2022)

[5]

Lee J., Nishikawa R.M., Identifying women with mammographically-occult breast cancer leveraging GAN-simulated mammograms, IEEE Trans. Med. Imaging, 41, 1, pp. 225-236, (2021)

[6]

Lu M., Et al., Detection and localization of breast cancer using UWB microwave technology and CNN-LSTM framework, IEEE Trans. Microwave Theory Tech, 70, 11, pp. 5085-5094, (2022)

[7]

Singla C., Sarangi P.K., Sahoo A.K., Singh P.K., Deep learning enhancement on mammogram images for breast cancer detection, Mater. Today Proc, 49, pp. 3098-3104, (2022)

[8]

Su Y., Liu Q., Xie W., Hu P., YOLO-LOGO: A transformer-based YOLO segmentation model for breast mass detection and segmentation in digital mammograms, Comput. Methods Program. Biomed, 221, (2022)

[9]

Raaj R.S., Breast cancer detection and diagnosis using hybrid deep learning architecture, Biomed. Signal Process. Control, 82, (2023)

[10]

Gurudas V.R., Shaila S.G., Vadivel A.J.S.C.S., Breast cancer detection and classification from mammogram images using multi-model shape features, SN Comput. Sci, 3, 5, (2022)

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