Transfer Learning in Breast Cancer Diagnoses via Ultrasound Imaging
Abstract
Simple Summary
Abstract
1. Introduction
2. Transfer Learning
2.1. Overview of Transfer Learning
2.2. Advantages of Transfer Learning
2.3. Transfer Learning Approaches
2.3.1. Feature Extracting
2.3.2. Fine-Tuning
2.3.3. Feature Extracting vs. Fine-Tuning
2.4. Pre-Training Model and Dataset
- ImageNet: ImageNet is a large image database designed for use in image recognition [77,78,79]. It comprise more than 14 million images that have been hand-annotated to indicate the pictured objects. ImageNet is categorized into more than 20,000 categories with a typical category consisting of several images. The third-party image URLs repository of annotations is freely accessible directly from ImageNet, although ImageNet does not own the images.
2.5. Pre-Processing
2.6. Convolutional Neural Network
- AlexNet: the AlexNet architecture is composed of eight layers. The first layers of AlexNet are the convolutional layers, and the next layer is a max-pooling layer for data dimension reduction [77,78,79]. AlexNet uses a rectified linear unit (ReLU) for the activation function, which offers faster training than other activation functions. The remaining three layers are the fully connected layers.
- VGGNet: VGG16 was the first CNN introduced by the Visual Geometry Group (VGG); this was followed by VGG19; VGG16 and VGG19 becoming two excellent architectures on ImageNet [85]. VGGNet models afford better performance than AlexNet by superseding large kernel-sized filters with various small kernel-sized filters; thus, VGG16 and VGG19 comprise 13 and 16 convolution layers, respectively [84,85,86].
- Inception: this is a GoogLeNet model focused on improving the efficiency of VGGNet from the perspective of memory usage and runtime without reducing performance accuracy [86,87,88,89]. To achieve this, it removes the activation functions of VGGNet that are iterative or zero [86]. Therefore, GoogLeNet came up with and added a module known as Inception, which approximates scattered connections between the activation functions [87]. Following InceptionV1, the architecture was improved in three subsequent versions [88,89]. InceptionV2 used batch normalization for training, and InceptionV3 introduced the factorization method to enhance the computational complexity of convolution layers. InceptionV4 brought about a similar comprehensive type of Inception-V3 architecture with a larger number of inception modules [89].
3. Results
4. Discussion
5. Outlook
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
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Study | TL Approach Used | Pre-Training Model Used | Application | Image Dataset | Pre-Processing | Pre-Training Dataset |
---|---|---|---|---|---|---|
Byra et al. [26] | Fine-tuning | VGG19 & InceptionV3 | Classification | OASBUD | Compression and augmentation | ImageNet |
Byra et al. [24] | Fine-tuning | VGG19 | Classification | 882 US images of their own and public images UDIAT and OASBUD | Matching layer | ImageNet |
Hijab et al. [27] | Fine-tuning | VGG16 | Classification | 1300 US Images | Augmentation | ImageNet |
Yap et al. [25] | Fine-tuning | AlexNet | Detection | Dataset A and B | Splitting in to patches | ImageNet |
Yap et al. [28] | Fine-tuning | AlexNet | Detection | Dataset A and B | Ground-truth labeling | ImageNet |
Huynh et al. [23] | Feature extractor | AlexNet | Classification | Breast mammogram dataset with 2393 regions of interest (ROIs) | Compression and augmentation | ImageNet |
Hadad et al. [29] | Fine-tuning | VGG128 | Detection and classification | MRI data | Augmentation | Medical Image (Mammography image) |
Study | Performance Analysis Approach | Performance Metrics | Results |
---|---|---|---|
Byra et al. [26] | Classification performance of classifiers developed using train all and evaluated on test all. | AUC, sensitivity, accuracy, and specificity | InceptionV3: AUC = 0.857 VGG19: AUC = 0.822 |
Byra et al. [24] | Classification performance with and without the matching layer (ML) on two datasets. Bootstrap was used to calculate the parameter standard deviations. | AUC, sensitivity, accuracy, and specificity | The better-performing fine-tuning approach and matching layer, had AUC = 0.936 on a test data of 150 cases |
Hijab et al. [27] | Comparison between accuracy of their model using ultrasound images and other related work. | AUC and accuracy | AUC = 0.98 Accuracy = 0.9739 |
Yap et al. [25] | Comparison of the capability of the proposed deep learning models on the combined dataset. | TPF, FPs/image, and F-measure | FCN-AlexNet (A + B): (TPF = 0.99 for A and TPF = 0.93 for B) |
Yap et al. [28] | Dice similarity coefficient to compare with the malignant lesions. | Mean Dice, sensitivity, precision, and Matthew correlation coefficient (MCC) | “Mean Dice” score of 0.7626 with FCN-16s |
Huynh et al. [23] | Classifiers trained on pre-trained models features were compared with classifiers trained with human-designed features. | AUC | SVM trained on human-designed features obtained an AUC = 0.90. SVM trained on CNN-extracted features obtained an AUC = 0.88 |
Hadad et al. [29] | Cross-modal and cross-domain transfers learning were compared. | Accuracy | Cross-modal = 0.93 Cross-domain = 0.90 |
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Ayana, G.; Dese, K.; Choe, S.-w. Transfer Learning in Breast Cancer Diagnoses via Ultrasound Imaging. Cancers 2021, 13, 738. https://doi.org/10.3390/cancers13040738
Ayana G, Dese K, Choe S-w. Transfer Learning in Breast Cancer Diagnoses via Ultrasound Imaging. Cancers. 2021; 13(4):738. https://doi.org/10.3390/cancers13040738
Chicago/Turabian StyleAyana, Gelan, Kokeb Dese, and Se-woon Choe. 2021. "Transfer Learning in Breast Cancer Diagnoses via Ultrasound Imaging" Cancers 13, no. 4: 738. https://doi.org/10.3390/cancers13040738
APA StyleAyana, G., Dese, K., & Choe, S.-w. (2021). Transfer Learning in Breast Cancer Diagnoses via Ultrasound Imaging. Cancers, 13(4), 738. https://doi.org/10.3390/cancers13040738