Holographic Microwave Image Classification Using a Convolutional Neural Network

Wang, Lulu

doi:10.3390/mi13122049

Cited by 6 publications

(6 citation statements)

References 37 publications

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“…Rana et al [ 93 ] applied ML in radar-based imaging for breast lesion detection. Wang [ 11 ] proposed a modified AlexNet with transfer learning to automatically detect, classify, and quantify different high-resolution microwave imaging breast images. The proposed transfer learning network achieved 100% accuracy in identifying and classifying HMI images, demonstrating promising applications in microwave breast imaging.…”

Section: Microwave Breast Imaging Techniquesmentioning

confidence: 99%

“…This enables the identification of malignant cells, which is crucial for an accurate diagnosis. Although holographic microwave imaging (HMI) algorithms have shown potential for breast cancer detection, further development, and validation are necessary before these techniques can be implemented in clinical trials [8][9][10][11][12][13]. Some MBI systems have already reached the clinical trials stage, as shown in Figure 2 [14,15].…”

Section: Introductionmentioning

confidence: 99%

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Microwave Imaging and Sensing Techniques for Breast Cancer Detection

Wang

2023

Micromachines

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Medical imaging techniques, including X-ray mammography, ultrasound, and magnetic resonance imaging, play a crucial role in the timely identification and monitoring of breast cancer. However, these conventional imaging modalities have their limitations, and there is a need for a more accurate and sensitive alternative. Microwave imaging has emerged as a promising technique for breast cancer detection due to its non-ionizing, non-invasive, and cost-effective nature. Recent advancements in microwave imaging and sensing techniques have opened up new possibilities for the early diagnosis and treatment of breast cancer. By combining microwave sensing with machine learning techniques, microwave imaging approaches can rapidly and affordably identify and classify breast tumors. This manuscript provides a comprehensive overview of the latest developments in microwave imaging and sensing techniques for the early detection of breast cancer. It discusses the principles and applications of microwave imaging and highlights its advantages over conventional imaging modalities. The manuscript also delves into integrating machine learning algorithms to enhance the accuracy and efficiency of microwave imaging in breast cancer detection.

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Section: Microwave Breast Imaging Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Microwave Imaging and Sensing Techniques for Breast Cancer Detection

Wang

2023

Micromachines

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“…Transfer learning addresses this challenge by allowing the transfer of knowledge from larger and more diverse datasets. Models pre-trained on millions of images can be adapted for breast cancer detection, even when the specific dataset is relatively small [54,55]. This increases the efficiency of model training and enables the development of robust classifiers, particularly in cases where collecting large-scale, specialized medical datasets is challenging.…”

Section: Transfer Learning For Breast Cancer Detectionmentioning

confidence: 99%

Deep Learning Theories and Methods for Breast Cancer Classification

Wang

2023

Preprint

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Breast cancer is a common malignant tumour and studies have shown that early and accurate detection is crucial for patients. With the maturity of medical imaging and deep learning development, significant progress has been made in breast cancer classification, which greatly improves the accuracy and efficiency of classification. This review focuses on deep learning, migration learning, GAN, and lifelong learning to elaborate and summarise the important roles arising from breast cancer detection. This review also examines the dataset and labeling issues required for breast cancer classification. In conclusion, at the end of the article, we look at future directions for breast cancer classification research, including cross-migration learning, multimodal data fusion, model interpretability, and lifelong learning, and also explore how to provide personalized treatment plans for patients.

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“…This kind of inverse problem is well-known in the scientific literature as the "inverse scattering problem" (ISP), and finding a stable solution is not trivial due to the issues of nonlinearity and ill-posedness [9]. Several inversion algorithms can be adopted to solve the ISP under consideration [10][11][12][13][14][15][16], and among them, neural networks seem to have better image reconstruction performance compared to traditional approaches [17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

“…Existing research in the field of microwave imaging has demonstrated the potential of artificial neural networks in reconstructing realistic breast images [20,21,23]. However, the reconstruction performance of these models in terms of both spatial resolution and retrieved complex permittivity values often remain insufficient to allow for accurate differentiation between breast tumors and the surrounding fibro-glandular tissue.…”

Section: Introductionmentioning

confidence: 99%

Microwave Breast Sensing via Deep Learning for Tumor Spatial Localization by Probability Maps

Borghouts,

Ambrosanio,

Franceschini

et al. 2023

Bioengineering

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Background: microwave imaging (MWI) has emerged as a promising modality for breast cancer screening, offering cost-effective, rapid, safe and comfortable exams. However, the practical application of MWI for tumor detection and localization is hampered by its inherent low resolution and low detection capability. Methods: this study aims to generate an accurate tumor probability map directly from the scattering matrix. This direct conversion makes the probability map independent of specific image formation techniques and thus potentially complementary to any image formation technique. An approach based on a convolutional neural network (CNN) is used to convert the scattering matrix into a tumor probability map. The proposed deep learning model is trained using a large realistic numerical dataset of two-dimensional (2D) breast slices. The performance of the model is assessed through visual inspection and quantitative measures to assess the predictive quality at various levels of detail. Results: the results demonstrate a remarkably high accuracy (0.9995) in classifying profiles as healthy or diseased, and exhibit the model’s ability to accurately locate the core of a single tumor (within 0.9 cm for most cases). Conclusion: overall, this research demonstrates that an approach based on neural networks (NN) for direct conversion from scattering matrices to tumor probability maps holds promise in advancing state-of-the-art tumor detection algorithms in the MWI domain.

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Holographic Microwave Image Classification Using a Convolutional Neural Network

Cited by 6 publications

References 37 publications

Microwave Imaging and Sensing Techniques for Breast Cancer Detection

Microwave Imaging and Sensing Techniques for Breast Cancer Detection

Deep Learning Theories and Methods for Breast Cancer Classification

Microwave Breast Sensing via Deep Learning for Tumor Spatial Localization by Probability Maps

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