Hash Code Generation using Deep Feature Selection Guided Siamese Network for Content-Based Medical Image Retrieval

It is very pleasing for human health that medical knowledge has increased and the technological infrastructure improves systems. The widespread use of imaging devices been instrumental in saving lives by allowing early diagnosis many diseases. These images are stored large databases purposes. datasets used when a suspicious diagnostic case encountered or to gain experience inexperienced radiologists. To fulfill these tasks, similar one query image searched from within dataset. Accuracy speed vital this process, which called content-based retrieval (CBIR). In literature, best way perform CBIR system using hash codes. This study provides an effective code generation method based on feature selection-based downsampling deep features extracted images. Firstly, pre-hash codes 256-bit length each generated pairwise siamese network architecture works similarity two Having between -1 1 makes it easy generate hashing algorithms. For reason, all activation functions proposed convolutional neural (CNN) selected as hyperbolic tanh. Finally, neighborhood component analysis (NCA) selection methods convert binary code. also downsamples 32-bit, 64-bit, 96-bit levels. performance evaluated NEMA MRI CT datasets.