  Quantitative ultrasound (QUS) can reveal crucial information on tissue
properties such as scatterer density. If the scatterer density per resolution
cell is above or below 10, the tissue is considered as fully developed speckle
(FDS) or low-density scatterers (LDS), respectively. Conventionally, the
scatterer density has been classified using estimated statistical parameters of
the amplitude of backscattered echoes. However, if the patch size is small, the
estimation is not accurate. These parameters are also highly dependent on
imaging settings. In this paper, we propose a convolutional neural network
(CNN) architecture for QUS, and train it using simulation data. We further
improve the network performance by utilizing patch statistics as additional
input channels. We evaluate the network using simulation data, experimental
phantoms and in vivo data. We also compare our proposed network with different
classic and deep learning models, and demonstrate its superior performance in
classification of tissues with different scatterer density values. The results
also show that the proposed network is able to work with different imaging
parameters with no need for a reference phantom. This work demonstrates the
potential of CNNs in classifying scatterer density in ultrasound images.
