As the amount of medical images transmitted over networks and kept on online servers continues to rise, the need to protect those images digitally is becoming increasingly important. However, due to the massive amounts of multimedia and medical pictures being exchanged, low computational complexity techniques have been developed. Most commonly used algorithms offer very little security and require a great deal of communication, all of which add to the high processing costs associated with using them. First, a deep learning classifier is used to classify records according to the degree of concealment they require. Medical images that aren’t needed can be saved by using this method, which cuts down on security costs. Encryption is one of the most effective methods for protecting medical images after this step. Confusion and dispersion are two fundamental encryption processes. A new encryption algorithm for very sensitive data is developed in this study. Picture splitting with image blocks is now developed by using Zigzag patterns, rotation of the image blocks, and random permutation for scrambling the blocks. After that, this research suggests a Region of Interest (ROI) technique based on selective picture encryption. For the first step, we use an active contour picture segmentation to separate the ROI from the Region of Background (ROB). Permutation and diffusion are then carried out using a Hilbert curve and a Skew Tent map. Once all of the blocks have been encrypted, they are combined to create encrypted images. The investigational analysis is carried out to test the competence of the projected ideal with existing techniques.

The Coronavirus Disease (COVID-19) pandemic has exposed the vulnerabilities of medical services across the globe, especially in underdeveloped nations. In the aftermath of the COVID-19 outbreak, a strong demand exists for developing novel computer-assisted diagnostic tools to execute rapid and cost-effective screenings in locations where many screenings cannot be executed using conventional methods. Medical imaging has become a crucial component in the disease diagnosis process, whereas X-rays and Computed Tomography (CT) scan imaging are employed in a deep network to diagnose the diseases. In general, four steps are followed in image-based diagnostics and disease classification processes by making use of the neural networks, such as network training, feature extraction, model performance testing and optimal feature selection. The current research article devises a Chaotic Flower Pollination Algorithm with a Deep Learning-Driven Fusion (CFPA-DLDF) approach for detecting and classifying COVID-19. The presented CFPA-DLDF model is developed by integrating two DL models to recognize COVID-19 in medical images. Initially, the proposed CFPA-DLDF technique employs the Gabor Filtering (GF) approach to pre-process the input images. In addition, a weighted voting-based ensemble model is employed for feature extraction, in which both VGG-19 and the MixNet models are included. Finally, the CFPA with Recurrent Neural Network (RNN) model is utilized for classification, showing the work’s novelty. A comparative analysis was conducted to demonstrate the enhanced performance of the proposed CFPA-DLDF model, and the results established the supremacy of the proposed CFPA-DLDF model over recent approaches.