Automatic Recognition of Abdominal Organs in Ultrasound Images based on Deep Neural Networks and K-Nearest-Neighbor Classification

TL;DR

This paper presents a real-time method combining deep neural networks and k-NN to automatically recognize six abdominal organs in ultrasound images with high accuracy, aiming to assist sonographers and reduce examination time.

Contribution

The study introduces a novel combination of fine-tuned deep neural networks and k-NN classification for ultrasound organ recognition, demonstrating high accuracy with minimal training.

Findings

Achieved 96.67% classification accuracy.

Effective feature extraction using PCA and deep learning.

Real-time recognition capability demonstrated.

Abstract

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.