Variable-frame CNNLSTM for Breast Nodule Classification using Ultrasound Videos

TL;DR

This paper introduces a novel CNN-LSTM method for classifying ultrasound breast nodule videos that handles variable frame lengths efficiently, improving accuracy and computational performance over existing keyframe and uniform-frame methods.

Contribution

It pioneers the integration of NLP-inspired sequence processing into ultrasound video classification, enabling variable frame length handling and enhanced feature extraction.

Findings

Outperforms keyframe methods with 3-6% higher F1 score

Achieves 1.5% higher specificity than equal-frame CNNLSTM

Demonstrates improved accuracy and precision in classifying ultrasound videos

Abstract

The intersection of medical imaging and artificial intelligence has become an important research direction in intelligent medical treatment, particularly in the analysis of medical images using deep learning for clinical diagnosis. Despite the advances, existing keyframe classification methods lack extraction of time series features, while ultrasonic video classification based on three-dimensional convolution requires uniform frame numbers across patients, resulting in poor feature extraction efficiency and model classification performance. This study proposes a novel video classification method based on CNN and LSTM, introducing NLP's long and short sentence processing scheme into video classification for the first time. The method reduces CNN-extracted image features to 1x512 dimension, followed by sorting and compressing feature vectors for LSTM training. Specifically, feature vectors are sorted by patient video frame numbers and populated with padding value 0 to form variable batches, with invalid padding values compressed before LSTM training to conserve computing resources. Experimental results demonstrate that our variable-frame CNNLSTM method outperforms other approaches across all metrics, showing improvements of 3-6% in F1 score and 1.5% in specificity compared to keyframe methods. The variable-frame CNNLSTM also achieves better accuracy and precision than equal-frame CNNLSTM. These findings validate the effectiveness of our approach in classifying variable-frame ultrasound videos and suggest potential applications in other medical imaging modalities.