Automated ROI detection in left hand X-ray images using CNN and RNN

Abstract

Automatic segmentation of the area of interest in medical image processing is a very important but difficult problem. Deep learning algorithms can help clinicians and radiologists determine diagnosis and treatment plans. We propose and evaluate a probabilistic approach for automated region of interest ROIs detection using convolutional neural networks (CNNs). The proposed algorithm is simple and can be divide into regions and features can be extracted for the divided regions. We also propose a preprocessing algorithm based on CNN and RNN to automatically classify ROIs that are finely adjusted through image standardization based on TW3. The result is 20%-40% more accurate than those obtained using the conventional method. In addition, input image sensitivity is approximately 40% greater and the specificity was equal to or greater than 96%.