Description
- Abstract:
- Objective: The objective of this study was to create a convolutional neural network (CNN) capable of predicting the size, elastic modulus, and the location of flaws embedded in soft materials. One potential application for this methodology is early breast cancer detection. This would make breast cancer screening and diagnostic safe for those who cannot undergo ionizing radiation and more accessible to those who do not have access to mammography. Methods: Simulations of a breast containing a tumor of various elastic moduli and sizes were created. A pressure load simulating an ultrasound signal emitted from a probe was applied to the outside surface of the breast. The resulting signal, along with known elastic modulus and size of the tumor, was used to train a convolutional neural network. Convolutional neural networks consist of convolution layers where the input data is being transform by element-wise multiplication with convolution kernel, pooling layers that down sample the resulting data, and fully connected (FC) layers that connect the input and output. To experimentally validate the model, tissue mimicking phantoms will be created and imaged using an Olympus-1 ultrasound machine. Results: MAPE for the training data set was 0.733 and 5.66 for size and elastic modulus, respectively. MAPE for the testing data set was 1.11 and 7.83 for size and elastic modulus, respectively. Conclusions: The convolutional neural network trained on simulation data was evaluated to be highly effective and accurate. Future work is required to experimentally validate and expand the model to be able to characterize smaller tumors and tumor density.
- Notes:
- Thesis (Sc. M.)--Brown University, 2022
Citation
Rusnak, Anna,
"Using Convolutional Neural Network for Early Breast Cancer Detection"
(2022).
Biomedical Engineering Theses and Dissertations.
Brown Digital Repository. Brown University Library.
https://repository.library.brown.edu/studio/item/bdr:c25784pe/
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Biomedical Engineering Theses and Dissertations
Theses and Dissertations for the Biomedical Engineering department....