# Cryptographic Encryption and Optimization for Internet of Things Based Medical Image Security
## 5 Results and Analysis  
MATLAB 2016 supports the recommended best key-based security strategy with an i5 processor and 4 GB of RAM. Medical photographs from websites, including brain, lung EEG images, and other images, were gathered for security examination. As seen in Figure 3, the researchers looked at a variety of diagnostic scans, including those for the brain, lung, glaucoma, and cancer. They came from medical organizations using cloud storage. The secret image was examined both before being conveyed and after being received by the intended receiver. This is done to ensure that the first cover record has a tiny distortion once the hidden image has been masked over. Results of the suggested approach are shown in Table 1 using several picture measurements: time, PSNR, MSE, BER, CC, and SSI. The optimal key is selected to divide the message into three equal portions with the highest level of fitness, and after that, the entire input is encrypted. These test images have a maximum PSNR of 63.25 dB, which is significantly greater than the \"MSE and BER\" of 0.15 and 0 and the most exciting SSI of 1. Additionally, this table shows the cover picture and scrambled image histograms. The convergence of the fitness function (PSNR) is shown in Figure 4 in comparison to the obstacle bloom breeding optimization model and conventional optimization. The obstacle bloom breeding strategy in this diagram requires the fewest iterations possible to produce the best results. In keeping with this, it is increased to 63.25, which was accomplished in 78 cycles, the hybrid's initial fitness estimation is 11.36, whereas the underlying fitness calculation for other approaches is 9.48. Then, with variations in light of the tactics, the emphasis is altered in accordance with the implementation. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Images** & **Encrypted Image** & **PSNR** & **MSE** & **BER** & **SSI** & **CC** \\ \hline  & & 59.23 & 0.12 & 0 & 1 & 0.95 \\ \hline  & & 58.22 & 0.09 & 0 & 1 & 1 \\ \hline  & & 59.34 & 0.15 & 0.01 & 1 & 0.98 \\ \hline \end{tabular}
\end{table} 
Table 1: Results of the proposed model (hostile orchestration and obstruction bloom breeding optimization). Figure 3: Images from a sample database for the suggested model of (**a**) The brain image, (**b**) Glaucoma image, (**c**) Echocardiography image. Figure 5a-e shows a comparable analysis using different measurements. Here, different approaches--elliptic curve cryptography (ECC) [29; 30], ECC + PSO, and OBBO with hybrid improvement--are taken into consideration to handle the correlation part. Figure 5a shows how a hybrid technique that contrasted with the current strategy was used to examine the PSNR measure, where the most extreme result is 63.25 dB. The output images are recognized and the proposed approach is connected to the image by the images and by their PSNR values. By 2.89%, it differs from ECC, ECC + PSO, and OBBO. The MSE and BER are shown in Figure 5b,c. All the images of the suggested model are currently valued. There was no variety between the considered images in the BER, where its qualities were zero for the two images, meaning there was no difference between the considered images. The MSE value for the suggested hybrid encryption approach, which is 0.68, is the highest. In Figure 5d, neighboring pixel situations hold significant information about the structural substance of the image. The encryption and decryption process over this lengthy computing time in the least possible time, which is 1.5 min, as illustrated in Figure 5e. According to the current research, the Rivest-Shamir-Adleman-based Arnold map (RSA-AM), hostile orchestration (HO), and OBBO technique provided are faster at encrypting and decrypting data than existing technologies.