# Deep Learning for Medical Image Cryptography: A Comprehensive Review
## 4 Deep Learning for Medical Image Cryptography
### Image Encryption and Compression in Deep Learning-Based Medical Image Processing  
The security of medical images for transmission and storage is a crucial concern, given the exponential proliferation of images. An effective strategy frequently used to protect medical image data is encryption. These medical images take up a lot of storage space, so compression is required before transmission. In this section, recently reported work in this direction is presented. [PERSON] et al. [110] developed an adaptive sigma filterized synorr certificateless signcryptive Levenshtein entropy coding-based deep neural learning (ASPSCSLEC-DNL) technique to perform encryption and compression on medical images. The objective of the ASSCSLEC-DNL technique is to enhance the security of the medical images during transmission. The proposed approach is applied on chest X-ray medical images and the results are found to be encouraging and comparable with the existing state-of-the-artwork. A block-based perceptual encryption (PE) algorithm approach that may be used with both grayscale and color medical images was proposed by [PERSON] et al. [140]. The JPEG compression technology is used to reduce the size of these images. Chest X-ray images are used for the implementation of a deep learning-based system for TB screening. On three CXR image datasets, the suggested technique is examined in terms of encryption, compression, and DL-based categorization. The lossy manner of compression was used, and it was discovered that the performance of restored images is unaffected by distortion. [PERSON] et al. [141] proposed a secure framework, i.e., MediSecFed, for federated learning in a hostile environment. This model relies on the ideas from knowledge distillation and model inversion to ensure additional security and privacy features. This method is evaluated on two chest X-ray datasets. The proposed method was also compared with FedAvg and it outperformed by 15% on both the datasets in a hostile environment. A novel medical image crypto-compression algorithm is proposed, which is based on artificial neural network (ANN) and the chaotic system, by [PERSON] et al. [142]. The main objective of the proposed approach is to preserve the sensitive information of medical \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Ref.**} & \multicolumn{1}{c}{**Year**} & \multicolumn{1}{c}{**Organ**} & \multicolumn{1}{c}{**Task**} & \multicolumn{1}{c}{**Image Modality**} & \multicolumn{1}{c}{**Datasets**} & \multicolumn{1}{c}{**Model**} & \multicolumn{1}{c}{**Metrics**} & \multicolumn{1}{c}{
\begin{tabular}{c} **Privacy Preserving/Security** \\ **Mechanism** \\ \end{tabular} } \\ \hline
[109] & 2019 & Eye & Classification & Fundoscopy & \begin{tabular}{c} ROP Dataset [137], \\ IDRiD [138] \\ \end{tabular} & CNN & \
\begin{tabular}{c} Accuracy, Memory \\ Efficiency, Latency \\ \end{tabular} &
\begin{tabular}{c} Fully \\ homomorphic \\ encryption \\ \end{tabular} \\
[135] & 2020 & Brain & Classification & MRI & \
\begin{tabular}{c} Kaggle brain \\ MRI dataset \\ \end{tabular} &
\begin{tabular}{c} Deep \\ CNN \\ \end{tabular} & Accuracy & RNS encryption \\
[136] & 2022 & Brain & Classification & Brain MRI & \
\begin{tabular}{c} NA \\ \end{tabular} &
\begin{tabular}{c} CNN \\ \end{tabular} & Accuracy & RNS-FHE \\
[134] & 2022 & \begin{tabular}{c} Multi- \\ organ \\ \end{tabular} & Detection & \begin{tabular}{c} Image CLEF medical \\ image dataset (contains \\ multi-modality images) \\ \end{tabular} & CLEF medical & \begin{tabular}{c} SecRCNN \\ image dataset [139] \\ \end{tabular} & \
\begin{tabular}{c} Computation \\ overhead \\ \end{tabular} &
\begin{tabular}{c} Additive secret \\ sharing-based \\ sub-protocols \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: List of Privacy-Preserving Object Detection and Classification in Medical Image Encryption Algorithms. images. The proposed approach was validated on both the steps, i.e., encryption and compression.