Canine Thoracic Radiographs Classification Using Deep Learning Algorithms: An Investigation
Abstract
Thoracic radiograph interpretation is a difficult and error-prone job for veterinarians. Even with recent developments in machine learning and computer vision, creating computer-aided diagnostic tools for radiographs is still a difficult and unresolved challenge, especially in veterinary medicine. This research aimed to develop a unique approach for categorizing canine thoracic radiographs (CTR) using Enhanced Layer wise deep neural Networks (EL-DNN). Thoracic radiographs of canine patients were collected retrospectively from 2010 to 2020. The radiograph data was split in half because it came from two distinct radiograph acquisition methods. The EL-DNNs' generalizability was evaluated using Data Set 2, whereas Data Set 1 was utilized for training and testing. We built and evaluated two alternative EL-DNNs, one using the ResNet-50 architecture and the other using the DenseNet-121. The area under the Receiver Operator Curve (AUC) values over 0.8 were achieved by the ResNet-50-based EL-DNN for all included radiographic findings on Data Sets 1 and 2, except bronchial and interstitial patterns. The overall performance of the DenseNet-121 EL-DNN was inferior. The EL-DNN trained on ResNet-50 outperformed the other regarding generalization ability, demonstrating superior performance for the alveolar, megaesophagus, interstitial, and pneumothorax.
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