Oppositional Jellyfish Search Optimizer with Deep Transfer Learning Enabled Secure Content-based Biomedical Image Retrieval

Recently, a drastic increase in medical imaging such as X-rays, Magnetic Resonance Imaging (MRI), and Computed Tomography (CT) offers essential anatomical functional details related to different body parts for monitoring, treatment planning, detection, classification. The use of Content-Based Medical Image Retrieval (CBMIR) technologies helps handle massive amounts images, also encryption can be considered an effective solution attain security the CBMIR process. In this regard, research develops optimal deep transfer learning enabled secure technique, called ODTL-SCBMIR model. proposed prototype aim is provide image techniques with retrieval procedures. To accomplish this, presented model initially employs multikey homomorphic (MKHE) oppositional jellyfish search optimizer (OJSO) algorithm security. Next, process includes series processes namely capsule network (CapsNet) based feature extraction, chaos game optimization (CGO) hyperparameter optimizer, Manhattan distance similar measurement. performance validation experimented by employing set images. investigational results implied enhanced over current approaches.