A Comparative Study of Deep Convolutional Neural Networks for Musculoskeletal X-Ray Images

Abstract

Technological achievements in science and technology, especially deep learning models have brought about a promising overall performance in many medical image evaluation tasks. Evaluating abnormal conditions from Body part X-rays images is such a need for radiological examination. To fulfill the need of automated detecting of Musculoskeletal conditions, this paper experiments and explores the state-ofthe-art deep neural networks which are most commonly applied for image processing. Some of the typical Convolutional neural networks are experimented and evaluated each of the model's classification performance using bone X-ray images. In this study, a typical CNN architecture, CNN architecture with Adabound optimizer, VGG16, ResNet50 and DenseNet architetures are experimented and evaluated. The effectiveness of these Deep CNN models are accessed and compared on the Cohen's kappa statistic and classification accuracy. DenseNet169 Model outperformed the other pre-trained models tested in this study, with the greatest Training Accuracy of 97.98%. However, the standard CNN model with Adam optimizer has a little higher kappa score than the DenseNet169 model. The DenseNet169 Model obtained roughly 20% in terms of Train and Test Loss, which is less than the training loss compared to the other pre-trained CNN models.