# Automatic Classification of Symmetry of Hemithoraces in Canine and Feline Radiographs

## TL;DR

This study introduces an automated method using CNNs and active contours to classify the symmetry of hemithoraces in canine and feline thoracic radiographs, aiming to improve positioning accuracy and reduce misdiagnosis.

## Contribution

The paper presents a novel combination of CNN-based segmentation and ensemble classification for symmetry assessment in veterinary radiographs, demonstrating robustness to exposure variations.

## Key findings

- Achieved an F1 score of 82.8% in symmetry classification.
- Segmentation robustness maintained with minimal IoU reduction under exposure corruption.
- Method applicable to human radiography with minimal modifications.

## Abstract

Purpose: Thoracic radiographs are commonly used to evaluate patients with confirmed or suspected thoracic pathology. Proper patient positioning is more challenging in canine and feline radiography than in humans due to less patient cooperation and body shape variation. Improper patient positioning during radiograph acquisition has the potential to lead to a misdiagnosis. Asymmetrical hemithoraces are one of the indications of obliquity for which we propose an automatic classification method. Approach: We propose a hemithoraces segmentation method based on Convolutional Neural Networks (CNNs) and active contours. We utilized the U-Net model to segment the ribs and spine and then utilized active contours to find left and right hemithoraces. We then extracted features from the left and right hemithoraces to train an ensemble classifier which includes Support Vector Machine, Gradient Boosting and Multi-Layer Perceptron. Five-fold cross-validation was used, thorax segmentation was evaluated by Intersection over Union (IoU), and symmetry classification was evaluated using Precision, Recall, Area under Curve and F1 score. Results: Classification of symmetry for 900 radiographs reported an F1 score of 82.8% . To test the robustness of the proposed thorax segmentation method to underexposure and overexposure, we synthetically corrupted properly exposed radiographs and evaluated results using IoU. The results showed that the models IoU for underexposure and overexposure dropped by 2.1% and 1.2%, respectively. Conclusions: Our results indicate that the proposed thorax segmentation method is robust to poor exposure radiographs. The proposed thorax segmentation method can be applied to human radiography with minimal changes.