![]() ![]() For an image size of 1024 × 1280, data augmentation reduced the mean pixel distance error from 8.3 (95% confidence interval ) to 5.34 (95% confidence interval ) for the regression model. ![]() We pay particular attention to consistent labelling to improve model performance. For the resulting landmark predictions, we evaluate shape bias using Procrustes analysis. We compared direct coordinate regression using a convolutional neural network and segmentation using a fully convolutional network for the second tier. The second tier provided landmark coordinates for the remaining wings. The first tier used a classification convolutional neural network to remove most wings that were missing landmarks. ![]() We developed a two-tier method using deep learning architectures to classify images and make accurate landmark predictions. The manual location of landmarks is time-consuming, prone to error, and infeasible for large data sets. Morphometric analysis required locating 11 anatomical landmarks on each wing. Single-wing images were captured from an extensive data set of field-collected tsetse wings of species Glossina pallidipes and G. Morphometric analysis of wings has been suggested for identifying and controlling isolated populations of tsetse ( Glossina spp), vectors of human and animal trypanosomiasis in Africa. ![]()
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