English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The Near-Infrared (NIR) Diffuse Optical Tomography (DOT) aims to reconstruct optical-property images of tissue and identify and localize breast tumors. This study has developed an efficient and fast DOT reconstruction method based on Deep Learning (DL) algorithm. The DL has already been applied in DOT application with limitations such as a limited dataset and the reconstruction of absorption coefficients only. We solve the problem of a limited dataset by generating a large dataset with multiple phantoms and inclusions at various positions. Moreover, a single Deep Neural Network (DNN) model has been designed to reconstruct absorption coefficients and scattering coefficients. For evaluation of the proposed DNN models, the phantom experimental dataset has been used where the results of the proposed DNN models outperform the results of the Tikhonov Regularization (TR) method and other Artificial Neural Networks (ANN). Moreover, it is shown that the DNN model with batch normalization layer results in improved spatial resolution, based on Contrast-and-Size Detail (CSD) analysis, as compared to DNN models without batch normalization layers.

Reconstruction and Localization of Tumors in Breast Optical Imaging via Convolution Neural Network Based on Batch Normalization Layers