Deep Learning based Approach for Bone Diagnosis Classification in Ultrasonic Computed Tomographic Images

Artificial intelligence (AI) in the area of medical imaging has shown a developed technology to have automatically the true diagnosis especially in ultrasonic imaging area. At this light, two types of neural networks algorithms have been developed to automatically classify the Ultrasonic Computed Tomographic (USCT) images into three categories, such as healthy, fractured and osteoporosis bone USCT images. In this work, at first step, a Convolutional Neural Network including two types of CNN models such (Inception-V3 and MobileNet) are proposed as a classifier system. At second step, an evolutionary neural network is proposed with the AmeobaNet model for USCT image classification. Results achieve 100% for train accuracy and 96%, 91.7% and 87.5% using Amoebanet, Inception-V3 and MobileNet respectively for the test accuracy. Results outperforms the state of the art and prove the robustness of the proposed classifier system with a short time process by its implementation on GPU.

@article{Fradi2020,
  title     = {Deep Learning based Approach for Bone Diagnosis Classification in Ultrasonic Computed Tomographic Images},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {12},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Marwa Fradi and Mouna Afif and Mohsen Machhout},
  doi       = {10.14569/IJACSA.2020.0111210},
  url       = {https://doi.org/10.14569/IJACSA.2020.0111210}
}