Efficient Transfer learning Model for Humerus Bone Fracture Detection

Abstract

Bone fracture classification with the help of the machine learning and deep learning models are common today. With the availability of pre-trained models such as VGG19, DenseNet121, DenseNet169 the trend is towards the concept of transfer learning. A transfer learning approach is followed in this work where the pre-trained models are first compared for finding a better model in terms of humerus bone fracture classification. In the first phase of the work, the three models are tested with the MURA Dataset. VGG16 performs comparatively low and much difference is not observed between DenseNet121 and DenseNet169. The hierarchy of results is also found to be similar in case of a customized dataset created from the humerus dataset by removing the images with metals. Hence, for complexity reasons, DenseNet121 is chosen for customization. Changes were made in the higher layers of the model and the model is subjected to partial training. While the lower layers are kept untrained the higher layers are trained with the humerus bone dataset and the customized dataset derived from it. Better results are obtained in both.