Abstract
The interpretation of thoracic radiographs is a challenging and error-prone task for veterinarians. Despite recent advancements in machine learning and computer vision, the development of computer-aided diagnostic systems for radiographs remains a challenging and unsolved problem, particularly in the context of veterinary medicine. In this study, a novel method, based on multi-label deep convolutional neural network (CNN), for the classification of thoracic radiographs in dogs was developed. All the thoracic radiographs of dogs performed between 2010 and 2020 in the institution were retrospectively collected. Radiographs were taken with two different radiograph acquisition systems and were divided into two data sets accordingly. One data set (Data Set 1) was used for training and testing and another data set (Data Set 2) was used to test the generalization ability of the CNNs. Radiographic findings used as non mutually exclusive labels to train the CNNs were: unremarkable, cardiomegaly, alveolar pattern, bronchial pattern, interstitial pattern, mass, pleural effusion, pneumothorax, and megaesophagus. Two different CNNs, based on ResNet-50 and DenseNet-121 architectures respectively, were developed and tested. The CNN based on ResNet-50 had an Area Under the Receive-Operator Curve (AUC) above 0.8 for all the included radiographic findings except for bronchial and interstitial patterns both on Data Set 1 and Data Set 2. The CNN based on DenseNet-121 had a lower overall performance. Statistically significant differences in the generalization ability between the two CNNs were evident, with the CNN based on ResNet-50 showing better performance for alveolar pattern, interstitial pattern, megaesophagus, and pneumothorax.
Highlights
Computed tomographic images, have been approved by the Food and Drug Administration in the last few years, thereafter becoming commercially available
To the best of the authors’ knowledge, both in human[11,12,22] and in veterinary medicine[22,23], most of the studies on applying convolutional neural networks (CNN) to thoracic radiographs are focused on detecting individual pathologies or conditions, whereas studies using a multi-label approach are relatively scarce in the human medical literature[16,21,24,25] and the scope to use multi-label algorithms on canine thoracic radiographs has not been explored yet
There was an uneven distribution of the different radiographic findings between the two data sets, with some over-represented and some under-represented in Data Set 2 when compared to Data Set 1
Summary
Computed tomographic images, have been approved by the Food and Drug Administration in the last few years, thereafter becoming commercially available. The possibilities offered by deep learning in veterinary medicine have been investigated for the classification of magnetic resonance images[17,18] for the detection of liver degeneration from ultrasound images[19] and for the automatic classification of corneal lesions from p hotographs[20]. The aims of this study are: (1) to develop a multi-label deep learning-based network capable of detecting some of the most common lesions found on plain radiographs of the canine thorax; (2) to test the generalization ability of the developed algorithm on an external Data Set of radiographs
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