# Deep Learning for Medical Image Cryptography: A Comprehensive Review
## 4 Deep Learning for Medical Image Cryptography
### End-to-End Image Encryption for Deep Learning-Based Medical Image Analysis  
Deep learning-based image encryption methods have recently drawn a lot of attention. When utilizing end-to end image encryption methods, the content is hidden until it is decrypted using the correct and permitted key. Using an encryption key, the original image is converted into a cipher image, which is then decrypted to reveal the original image using a decryption key. An image encryption method based on the Cycle-GAN network was proposed by [PERSON] et al. [147]. The encryption and decryption network can effectively encrypt and decrypt an input image after training. The encrypted images are the same size as the original images in terms of size. The secret keys in this method serve as the trainable parameters. To achieve end-to-end encryption, the authors made sure that the hospital database stored the encrypted images and that only a person with access to the keys could retrieve the original images by utilizing the proposed approach EncipherGAN. [PERSON] et al. [148] proposed the explainable privacy preserving image compression method named as DeepFixCX, which compresses images without learning by removing or obscuring spatial and edge information. DeepFiXCX improves predictive classification performance of a DNN on glaucoma and cervix-type detection and can improve multi-label chest X-ray classification performance. For end-to-end image diffusion, [PERSON] et al. [149] developed the flexible image encryption and decryption ResNet (FEDResNet) architecture. The security of the encryption network is improved in the proposed design by serial and parallel distribution of the images. The two crucial planes are built using a user-defined chaotic map to regulate access rights to images. After rigorous testing, they were able to attain high levels of security with acceptable efficiency. [PERSON] et al. [150] proposed present the community-driven generally nuanced deep learning framework (GaNDLF) as an end-to-end solution for scalable clinical workflows. The proposed GaNDLF is able to process the images of various domain including radiology scans and digitized histology WSIs along with the various workload, e.g., segmentation, regression and classification. [PERSON] et al. [151] proposed an end-to-end two-stage generative adversarial neural network (ToStaGAN) to improve the brain tumor segmentation from MRI images. In the proposed approach, UNET network is used in the first stage and U-shaped contextual autoencoder (ConEnDer) is used in the second stage. The performance of the proposed two-stage generation network is evaluated on BRATS2015 and it is found that the proposed network achieve better performance than the one-stage network. Table 7 summarizes the list of encryption or security algorithms applied along with the while performing deep learning-based images analysis for various tasks. It can be seen from the table that in this direction most of the work is reported very recently. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref. **} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Organ**} & \multirow{2}{*}{**Image Modality**} & \multirow{2}{*}{**Datasets**} & \multirow{2}{*}{**Task**} & \multirow{2}{*}{**Metrics**} & \multirow{2}{*}{**Encryption/Security**} & \multicolumn{2}{c}{**End-to-End**} \\  & & & & & & & & **Algorithm** & **Learning** \\ \hline
[151] & 2021 & Brain & MRI & BRATS2015 [152] & Segmentation & \begin{tabular}{c} Dice, Positive \\ value (PPV) and \\ Sensitivity. \\ Accuracy \\ \end{tabular}  & \ \begin{tabular}{c} Two-stage generative \\ adversarial neural \\ network (ToStaGAN) \\ Image diffusion with \\ dilated ResNet \\ \end{tabular}  & \begin{tabular}{c} UnNet and \\ Cu-shaped \\ autoencoder \\ FEDResNetNet \\ \end{tabular}  \\
[147] & 2023 & Skin & Dermoscopy & \begin{tabular}{c} Skin cancer dataset \\ [153] \\ Chespert [154], \\ Intel-MobilityOT \\ CT scan \\ \end{tabular}  & Classification, \