Detection of left atrial enlargement in dogs using deep learning in thoracic radiographs
DOI:
https://doi.org/10.26605/medvet-v19n1-6639Keywords:
computer-aided diagnosis, heart disease, image classifierAbstract
The study aimed to develop a tool to assist veterinarians in diagnosing left atrial enlargement on chest X-rays in dogs. The model utilized a total of 652 images all used in training and testing divided into two categories “positive” and “negative”. Three algorithms were used, obtaining the following results: the accuracy of the neural network was 89.7%, sensitivity of 90%, specificity of 89.5%, and Area Under the Curve (AUC) 95.8%. The accuracy of logistic regression was 88.2%, sensitivity 88.7%, specificity 87.8%, and AUC 94.1%. The decision tree accuracy was 69.6%, sensitivity 68.0%, specificity 71.0%, and AUC 69.6%. The classifier model with different algorithms can help radiologists improve the analysis of medical images by reducing errors, starting a selective double reading.Downloads
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