# Left-right relationship-aware 3D volume classification method

## TL;DR

This paper introduces a new 3D volume classification model that uses left-right symmetry in human anatomy to improve diagnostic accuracy.

## Contribution

A novel LR relationship-aware classification model with a multi-shift symmetric feature extraction module for 3D volume classification.

## Key findings

- The proposed model outperforms previous models in lung and brain 3D volume classification tasks.
- The multi-shift symmetric feature extraction module effectively handles positional gaps in anatomical structures.
- The model generalizes well for anatomical structures with bilateral or semi-symmetric properties.

## Abstract

This paper proposes a left-right (LR) relationship-aware classification model for 3D volumetric images (3D volume). Bilateral symmetry (LR relationship) is an essential property of the human body that can be used to detect abnormalities and understand anatomical structures. Checking the difference and similarity between the left and right anatomical structures is very important in diagnosis. We propose an LR relationship-aware classification model of 3D volume.

The proposed model employs an image feature extraction process from LR symmetric positions of human anatomy from 3D volume. Due to variations in body position and individual anatomical structure, small positional gaps among LR corresponding anatomical structures can be observed in medical images. We developed a multi-shift symmetric feature extraction module to accommodate such positional gaps.

The model was applied to 3D volume classification tasks of the lung and brain. From experimental results, the proposed model achieved superior performances both in the lung and brain classification tasks compared to the previous models. The result indicates that the proposed model has generalized performance in classifying anatomical structures with bilateral symmetric or semi-symmetric structures.

We proposed the LR relationship-aware classification model of 3D volume. The proposed model effectively extracts image features from LR symmetric positions. The multi-shift symmetric feature extraction module was employed to accommodate small positional gaps among LR corresponding positions. The experimental results of 3D volume classification tasks of the lung and brain showed that the proposed method achieved superior performances compared to the previous models. Our code is available at https://github.com/modafone/lr3dvolumeclassification.