Computer-Aided Diagnosis System for Sow Pregnancy Using Low-Frequency Portable Ultrasound Videos with Preprocessing and Augmentation Techniques

Accurate pregnancy diagnosis is important for reproductive management in pig farming. This study evaluated a computer-aided diagnosis workflow using portable low-frequency ultrasound videos from 1895 sows at two sites. Xception frame predictions were aggregated into video-level decisions. Original, Preprocessing (wavelet denoising and contrast-limited adaptive histogram equalization), Mixup, and their combination (Combined) were compared across ten repeated sow-level splits. Combined achieved mean accuracies of 84.5% and 85.3% in Datasets A and B, respectively, with pregnant-sow videos restricted to days 18–25 post-insemination. Compared with Original, mean specificity increased by 1.3 and 2.4 percentage points and Youden’s index by 1.7 and 2.3 points, while mean sensitivity was numerically similar. Paired 95% confidence intervals for specificity and Youden’s index differences excluded zero only in Dataset B, without adjustment for multiple comparisons. Paired comparisons of the area under the curve (AUC) did not establish improved discrimination. Mixup yielded the highest mean specificity and AUC, whereas Combined yielded higher mean sensitivity than Mixup. This trade-off varied across partition subsets. Cross-site and leave-one-session-out evaluations showed performance variability across acquisition settings, with no consistently best condition. These comparisons identify configuration-dependent trade-offs requiring further evaluation for diagnostic support.