This paper presents a fully-automated method for the identification of suspicious regions of a coronavirus disease (COVID-19) on chest CT volumes.
We utilize the scale uncertainty among various receptive field sizes of a segmentation FCN to obtain infection regions.
Our method recognizes and segments lung normal and infection regions in CT volumes.
Purpose: We propose a 2. 5D deep learning neural network (DLNN) to automatically classify thigh muscle into 11 classes and evaluate its classification accuracy over 2D and 3D DLNN when trained with limited datasets.