Adaptive bone abnormality detection in medical imagery using deep neural networks

摘要

This research conducts transfer learning with optimal training option identification for the detection of wrist bone abnormalities in X-Ray imagery. Specifically, transfer learning based on Convolutional Neural Networks (CNNs), such as ResNet-18 and GoogLeNet, has been developed for wrist bone abnormality detection. The effect of altering the number of epochs on the network performance using an automatic process is also investigated. The MURA wrist radiological images are extracted in our experiments. The proposed system achieves a superior performance for wrist bone abnormality detection in comparison with those of existing studies.