Year: 2020
Type: preprint
Abstract: Deep Neural Networks (DNN) are widely used to carry out segmentation tasks in biomedical images. Most DNNs developed for this purpose are based on some variation of the encoder-decoder U-Net architecture. Here we show that Res-CR-Net, a new type of fully convolutional neural network, which was originally developed for the semantic segmentation of microscopy images, and which does not adopt a U-Net architecture, is very effective at segmenting the lung fields in chest X-rays from either healthy patients or patients with a variety of lung pathologies.
Source: arXiv (Cornell University)
Authors: Abdulah, Haikal, Huber, Benjamin, Lal, Sinan, Abdallah, Hassan, Soltanian-Zadeh, Hamid, Gatti, Domenico L.
Institution: Wayne State University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3098106589