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Related Experiment Video

Updated: Aug 16, 2025

Evaluation and Manipulation of Neural Activity Using Two-Photon Holographic Microscopy
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Holographic Microwave Image Classification Using a Convolutional Neural Network.

Lulu Wang1

  • 1Biomedical Device Innovation Center, Shenzhen Technology University, Shenzhen 518118, China.

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Summary
This summary is machine-generated.

Convolutional neural networks (CNNs) show promise for classifying holographic microwave imaging (HMI) breast tumors. This study demonstrates CNNs can accurately identify benign and malignant tumors using small datasets and fast training times.

Keywords:
AlexNetbreast cancerdeep learningmicrowave imagingtransfer learning

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Area of Science:

  • Medical imaging
  • Artificial intelligence in healthcare
  • Biomedical engineering

Background:

  • Holographic microwave imaging (HMI) is a promising technique for early breast cancer diagnosis.
  • Automated classification of benign and malignant tumors in HMI is a significant challenge.
  • Convolutional neural networks (CNNs) have shown strong performance in image classification and tumor detection.

Purpose of the Study:

  • To investigate the feasibility of using CNNs for identifying and classifying HMI breast images.
  • To evaluate the performance of a modified AlexNet architecture with transfer learning for HMI tumor classification.

Main Methods:

  • A modified AlexNet architecture was employed, utilizing transfer learning.
  • The network was trained and evaluated on a dataset of 966 HMI breast images.
  • Performance was benchmarked against various pre-trained CNN models including ResNet, GoogLeNet, VGG19, DenseNet201, SqueezeNet, Inception v3, AlexNet, and Inception-ResNet-v2.

Main Results:

  • The proposed modified AlexNet network achieved high classification accuracy.
  • The network demonstrated effectiveness even with a small training dataset.
  • Fast training times were observed for the proposed CNN approach.

Conclusions:

  • CNNs, particularly the modified AlexNet with transfer learning, are feasible for automated classification of HMI breast images.
  • This approach offers a potential solution for accurate and efficient breast cancer diagnosis using HMI.
  • The study highlights the effectiveness of deep learning in analyzing complex medical imaging data.