Evaluation of deep neural network architectures in the identification of bone fissures

ABSTRACT Automated medical image processing, particularly of radiological images, can reduce the number of diagnostic errors, increase patient care and reduce medical costs. INTRODUCTION In recent years there have been more and more advantages of the use of digital image processing is used as a tool to support the diagnosis from medical images [1, 2] and it's even proved very valuable to track throughout images (temporal quantification and growth) both damage and behavior of tissues [3, 4]. [...]the networks selected for the visual categorization task of this performance test are ResNet (residual neural network), DenseNet (dense convolutional network), and NASNet (neural architecture search network). [...]the optimized NASNet architecture achieves the highest metric values (75% accuracy) with a much lower