Automatic identification, segmentation, and percentage calculation of healthy and unhealthy bovine lung tissue using machine learning algorithms

Abstract

Respiratory syndromes are a major cause of morbidity and mortality in the feedlot industry, making the accurate identification of lung lesions essential for effective health management. This study proposes an automated approach for identifying and segmenting pulmonary structures and lesions using a visual machine learning model trained on a limited dataset of post-mortem images captured at a slaughterhouse. The implementation was performed using Python 3.12 with computer vision libraries and the Ultralytics framework employing YOLO11n-seg. Training incorporated both dorsal and ventral views to identify lungs, trachea and extra tissues, and lesions (designated as “findings”). Data augmentation and k-fold cross-validation were applied to address the data limitations. In addition, we developed an automated algorithm that quantifies lung health by calculating the proportion of affected versus healthy tissue through a pixel overlap comparison between segmented lung regions and detected lesions. The proposed method enables objective and reproducible assessments of pulmonary conditions from different anatomical perspectives. The results demonstrate strong performance in both anatomical segmentation and lesion detection, while also highlighting the challenges inherent in data-constrained conditions. These findings contribute to the development of computer-aided tools for veterinary diagnostics.