Identification of Avascular Necrosis or Osteoporosis Using Deep Belief Convolutional Neural Networks

Abstract

Musculoskeletal impairment can be caused by Avascular Necrosis(AN). Younger people are more likely to develop it, thus early intervention and fast diagnosis are essential. The femoral bones are typically affected by this condition, which results in fractures that change the geometry of the bones. It is difficult to retrieve the AN-affected bone pictures because of the many places where the fractures are located. In this work, a useful method for retrieving AN pictures using deep belief CNN feature representation is proposed. Preprocessing is first applied to the raw dataset. In this stage, the median filter (MF) is used to reduce image noise and downsize the image. Using a deep belief convolutional neural network, features are represented (DB-CNN). The representations of the image feature data have now been converted to binary codes. Then, using the modified-hamming distance, the similarity measurement is calculated. The images are then retrieved with a focus on the similarity values. The test results demonstrated that the proposed approach is superior to the other methods now in use.