ENAS-B: Combining ENAS With Bayesian Optimization for Automatic Design of Optimal CNN Architectures for Breast Lesion Classification From Ultrasound Images.

IF 2.5 4区 医学 Q1 ACOUSTICS Ultrasonic Imaging Pub Date : 2024-01-01 Epub Date: 2023-11-20 DOI:10.1177/01617346231208709
Mohammed Ahmed, Hongbo Du, Alaa AlZoubi
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Abstract

Efficient Neural Architecture Search (ENAS) is a recent development in searching for optimal cell structures for Convolutional Neural Network (CNN) design. It has been successfully used in various applications including ultrasound image classification for breast lesions. However, the existing ENAS approach only optimizes cell structures rather than the whole CNN architecture nor its trainable hyperparameters. This paper presents a novel framework for automatic design of CNN architectures by combining strengths of ENAS and Bayesian Optimization in two-folds. Firstly, we use ENAS to search for optimal normal and reduction cells. Secondly, with the optimal cells and a suitable hyperparameter search space, we adopt Bayesian Optimization to find the optimal depth of the network and optimal configuration of the trainable hyperparameters. To test the validity of the proposed framework, a dataset of 1522 breast lesion ultrasound images is used for the searching and modeling. We then evaluate the robustness of the proposed approach by testing the optimized CNN model on three external datasets consisting of 727 benign and 506 malignant lesion images. We further compare the CNN model with the default ENAS-based CNN model, and then with CNN models based on the state-of-the-art architectures. The results (error rate of no more than 20.6% on internal tests and 17.3% on average of external tests) show that the proposed framework generates robust and light CNN models.

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ENAS- b:结合ENAS和贝叶斯优化自动设计乳腺病变超声图像分类的最优CNN架构。
高效神经结构搜索(ENAS)是卷积神经网络(CNN)设计中搜索最优细胞结构的最新发展。它已成功地应用于各种应用,包括乳腺病变的超声图像分类。然而,现有的ENAS方法只优化单元结构,而不是整个CNN架构或其可训练的超参数。本文结合ENAS和贝叶斯优化的优点,提出了一种新的CNN结构自动设计框架。首先,我们使用ENAS搜索最优的正常细胞和还原细胞。其次,利用最优单元和合适的超参数搜索空间,采用贝叶斯优化方法求出网络的最优深度和可训练超参数的最优配置;为了验证所提框架的有效性,使用1522张乳腺病变超声图像数据集进行搜索和建模。然后,我们通过在由727张良性和506张恶性病变图像组成的三个外部数据集上测试优化后的CNN模型来评估所提出方法的鲁棒性。我们进一步将CNN模型与默认的基于enas的CNN模型进行比较,然后与基于最先进架构的CNN模型进行比较。结果(内部测试错误率不超过20.6%,外部测试错误率平均不超过17.3%)表明,所提框架生成的CNN模型鲁棒轻巧。
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来源期刊
Ultrasonic Imaging
Ultrasonic Imaging 医学-工程:生物医学
CiteScore
5.10
自引率
8.70%
发文量
15
审稿时长
>12 weeks
期刊介绍: Ultrasonic Imaging provides rapid publication for original and exceptional papers concerned with the development and application of ultrasonic-imaging technology. Ultrasonic Imaging publishes articles in the following areas: theoretical and experimental aspects of advanced methods and instrumentation for imaging
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