BCDNet: An Optimized Deep Network for Ultrasound Breast Cancer Detection

IF 5.6 4区 医学 Q1 ENGINEERING, BIOMEDICAL Irbm Pub Date : 2023-08-01 DOI:10.1016/j.irbm.2023.100774
S.-Y. Lu , S.-H. Wang , Y.-D. Zhang
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引用次数: 1

Abstract

Objectives

Breast cancer is a common but deadly disease among women. Medical imaging is an effective method to diagnose breast cancer, but manual image screening is time-consuming. In this study, a novel computer-aided diagnosis system for breast cancer detection called BCDNet is proposed.

Material and Methods

We leverage pre-trained convolutional neural networks (CNNs) for representation learning and propose an adaptive backbone selection algorithm to obtain the best CNN model. An extreme learning machine serves as the classifier in the BCDNet, and a bat algorithm with chaotic maps is put forward to further optimize the parameters in the classifiers. A public ultrasound image dataset is used in the experiments based on 5-fold cross-validation.

Results

Simulation results suggest that our BCDNet outperforms several state-of-the-art breast cancer detection methods in terms of accuracy.

Conclusion

The proposed BCDNet is a useful auxiliary tool that can be applied in clinical screening for breast cancer.

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BCDNet:一个优化的超声乳腺癌检测深度网络
目的:癌症是一种常见但致命的女性疾病。医学影像学是诊断癌症的有效方法,但人工影像筛查耗时。本研究提出了一种新型的乳腺癌症计算机辅助诊断系统BCDNet。材料和方法我们利用预先训练的卷积神经网络(CNNs)进行表示学习,并提出了一种自适应骨干选择算法来获得最佳的CNN模型。BCDNet中使用了一个极限学习机作为分类器,并提出了一种带有混沌映射的bat算法来进一步优化分类器中的参数。实验中使用了基于5倍交叉验证的公共超声图像数据集。结果仿真结果表明,我们的BCDNet在准确性方面优于几种最先进的癌症检测方法。结论BCDNet是一种可用于癌症临床筛查的辅助工具。
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来源期刊
Irbm
Irbm ENGINEERING, BIOMEDICAL-
CiteScore
10.30
自引率
4.20%
发文量
81
审稿时长
57 days
期刊介绍: IRBM is the journal of the AGBM (Alliance for engineering in Biology an Medicine / Alliance pour le génie biologique et médical) and the SFGBM (BioMedical Engineering French Society / Société française de génie biologique médical) and the AFIB (French Association of Biomedical Engineers / Association française des ingénieurs biomédicaux). As a vehicle of information and knowledge in the field of biomedical technologies, IRBM is devoted to fundamental as well as clinical research. Biomedical engineering and use of new technologies are the cornerstones of IRBM, providing authors and users with the latest information. Its six issues per year propose reviews (state-of-the-art and current knowledge), original articles directed at fundamental research and articles focusing on biomedical engineering. All articles are submitted to peer reviewers acting as guarantors for IRBM''s scientific and medical content. The field covered by IRBM includes all the discipline of Biomedical engineering. Thereby, the type of papers published include those that cover the technological and methodological development in: -Physiological and Biological Signal processing (EEG, MEG, ECG…)- Medical Image processing- Biomechanics- Biomaterials- Medical Physics- Biophysics- Physiological and Biological Sensors- Information technologies in healthcare- Disability research- Computational physiology- …
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