  Abdominal ultrasound imaging has been widely used to assist in the diagnosis
and treatment of various abdominal organs. In order to shorten the examination
time and reduce the cognitive burden on the sonographers, we present a
classification method that combines the deep learning techniques and
k-Nearest-Neighbor (k-NN) classification to automatically recognize various
abdominal organs in the ultrasound images in real time. Fine-tuned deep neural
networks are used in combination with PCA dimension reduction to extract
high-level features from raw ultrasound images, and a k-NN classifier is
employed to predict the abdominal organ in the image. We demonstrate the
effectiveness of our method in the task of ultrasound image classification to
automatically recognize six abdominal organs. A comprehensive comparison of
different configurations is conducted to study the influence of different
feature extractors and classifiers on the classification accuracy. Both
quantitative and qualitative results show that with minimal training effort,
our method can "lazily" recognize the abdominal organs in the ultrasound images
in real time with an accuracy of 96.67%. Our implementation code is publicly
available at: https://github.com/LeeKeyu/abdominal_ultrasound_classification.
