Improving Generalization of ENAS-Based CNN Models for Breast Lesion Classification from Ultrasound Images

Ahmed, Mohammed Hussein; AlZoubi, Alaa; Du, Hongbo

doi:10.1007/978-3-030-80432-9_33

Cited by 4 publications

(7 citation statements)

References 20 publications

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“…To determine the classification error rates, all 5-fold were used. The imbalance ratio between benignity and malignancy (1.92:1) was upheld in the modeling and searching stages based on the findings in Ahmed et al 14 All images were pre-processed, and the training set enlarged using the pre-processing and data augmentation methods as described in the Section on Data Collection and Preparation.…”

Section: Methodsmentioning

confidence: 99%

“…We then compared ENAS-B with the original ENAS. 11 Based on our earlier findings as reported in Ahmed et al, 13 we chose ENAS17 for the comparison. Using the optimal cells as shown in Figure 3, ENAS17 architecture consists of 15 Normal cells (N) and two Reduction cells (R) in a configuration of (5N, R, 5N, R, 5N) and trained on the Modeling dataset under the same 5-fold cross validation protocol.…”

Section: Comparison With State-of-art Purposely Built Cnnsmentioning

confidence: 99%

“…A hybrid NAS framework for classifying and segmenting thyroid cancer from ultrasound images was proposed in Qian et al 12 ENAS with micro search space was adopted for breast lesion classification from US images. 13 The generalization gap of ENAS models was further investigated. 14 Nevertheless, the ENAS approach has its own limitations.…”

Section: Introductionmentioning

confidence: 99%

“…13 The generalization gap of ENAS models was further investigated. 14 Nevertheless, the ENAS approach has its own limitations. First, the number of blocks of cells is still determined manually through trials.…”

Section: Introductionmentioning

confidence: 99%

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

Ahmed,

Du,

AlZoubi

2023

Ultrason Imaging

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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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Section: Methodsmentioning

confidence: 99%

Section: Comparison With State-of-art Purposely Built Cnnsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Ahmed,

Du,

AlZoubi

2023

Ultrason Imaging

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“…6 To overcome the ENAS model generalization error problem, Ahmed et al examined the effectiveness of a range of techniques including reducing model complexity, use of data augmentation, and use of unbalanced training sets, and achieved remarkable results on the ultrasound image classification for breast lesions. 7 Although the ENAS method was proposed for designing the structure and weights of neural networks, 3 the idea of ENAS can be extended to design the structure and edge weights of any directed acyclic graph. Since the control system (CS) can be transformed into the form of a directed acyclic graph, this paper draws on the idea of ENAS to design the structure and parameters of the control system (CS) in an integrated manner, as shown in Figure 3.…”

Section: Introductionmentioning

confidence: 99%

A controller design method based on the integration of structure search and parameter optimization

Yang,

Wang,

Guo

et al. 2024

Advances in Mechanical Engineering

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For the controller structure design problem, a controller design method based on the integration of structure search and parameter optimization is proposed with the idea of ENAS, which automates the controller structure design work, and searches for the most suitable controller structure and completes the parameter optimization in a relatively short time. The simulation study with the galvano scanner as the controlled plant shows that the number of iterations and the consumption time of the method are only 15% of the traditional method in achieving the same performance compared with the traditional design method.

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Classification of breast lesions in ultrasound images using deep convolutional neural networks: transfer learning versus automatic architecture design

AlZoubi,

Lu,

Zhu

et al. 2023

Med Biol Eng Comput

Self Cite

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Deep convolutional neural networks (DCNNs) have demonstrated promising performance in classifying breast lesions in 2D ultrasound (US) images. Exiting approaches typically use pre-trained models based on architectures designed for natural images with transfer learning. Fewer attempts have been made to design customized architectures specifically for this purpose. This paper presents a comprehensive evaluation on transfer learning based solutions and automatically designed networks, analyzing the accuracy and robustness of different recognition models in three folds. First, we develop six different DCNN models (BNet, GNet, SqNet, DsNet, RsNet, IncReNet) based on transfer learning. Second, we adapt the Bayesian optimization method to optimize a CNN network (BONet) for classifying breast lesions. A retrospective dataset of 3034 US images collected from various hospitals is then used for evaluation. Extensive tests show that the BONet outperforms other models, exhibiting higher accuracy (83.33%), lower generalization gap (1.85%), shorter training time (66 min), and less model complexity (approximately 0.5 million weight parameters). We also compare the diagnostic performance of all models against that by three experienced radiologists. Finally, we explore the use of saliency maps to explain the classification decisions made by different models. Our investigation shows that saliency maps can assist in comprehending the classification decisions. Graphical Abstract

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Improving Generalization of ENAS-Based CNN Models for Breast Lesion Classification from Ultrasound Images

Cited by 4 publications

References 20 publications

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

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

A controller design method based on the integration of structure search and parameter optimization

Classification of breast lesions in ultrasound images using deep convolutional neural networks: transfer learning versus automatic architecture design

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