# Deep Learning for Medical Image Cryptography: A Comprehensive Review
## 4 Deep Learning for Medical Image Cryptography
### Medical Image Resolution Improvement or Denoising for Deep Learning-Based Medical Image Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Ref.** & **Year** & **Organ** & **Task** & **Image Modality** & **Datasets** & **Model** & **Metrics** & **Type of Encryption Algorithm** & **Algorithm Name** \\ \hline
[112] & 2020 & Chest & \begin{tabular}{c} Lung diseases \\ detection \\ \end{tabular} & X-ray & \begin{tabular}{c} NIH Chest X-rays \\ [17] \\ \end{tabular} & DenseNet-121 & \
\begin{tabular}{c} AUROC scores, \\ Accuracy, \\ Confusion matrix \\ \end{tabular} &
\begin{tabular}{c} Symmetric \\ algorithm \\ \end{tabular} & AES-128 \\
[115] & 2021 & Multi-organ & \begin{tabular}{c} Multi- \\ prediction task \\ \end{tabular} & \begin{tabular}{c} Multi- \\ modalities \\ \end{tabular} & \
\begin{tabular}{c} MIMIC-III (CCU \\ Dataset) [118] \\ \end{tabular} & RNN-LSTM & Precision, Recall &
\begin{tabular}{c} Homomorphic \\ encryption \\ \end{tabular} & CKKS \\
[113] & 2022 & Brain & \
\begin{tabular}{c} Brain tumor \\ segmentation \\ \end{tabular} & MRI & Real-Time Dataset & CNN & Accuracy rate &
\begin{tabular}{c} Symmetric \\ algorithm \\ \end{tabular} & AES-256 \\
[114] & 2022 & Brain & Tumor detection & MRI, EEG & \
\begin{tabular}{c} Brain Tumor \\ Dataset [119] \\ \end{tabular} &
\begin{tabular}{c} CNN-LSTM, \\ DNN \\ \end{tabular} & Accuracy rate & Hash functions & SHA-256 \\
[116] & 2022 & Chest & \begin{tabular}{c} COVID-19 New \\ cases forecasting \\ dataset \\ \end{tabular} & \begin{tabular}{c} PSNR/PIONT \\ \end{tabular} & NA & \
\begin{tabular}{c} Homomorphic \\ encryption \\ \end{tabular} &
\begin{tabular}{c} Rankerski/ \\ Fan-Vectaureren (BFV) \\ \end{tabular} \\
[107] & 2022 & Chest & \begin{tabular}{c} COVID-19 \\ Classification \\ \end{tabular} & X-ray & \begin{tabular}{c} Radiography \\ Database [121] \\ \end{tabular} & MobileNetV2 & \
\begin{tabular}{c} Precision, Recall, F1 \\ score, Support \\ \end{tabular} &
\begin{tabular}{c} Homomorphic \\ encryption \\ \end{tabular} & Paillier Method \\ \hline \hline \end{tabular}
\end{table} 
Table 2: A summary of medical image encryption techniques for deep learning-based medical image analysis. The multi-scale denoising convolutional neural network (MSDCNN) model was proposed by [PERSON] et al. (2014) for the detection of prostate cancer, and this model is also capable of suppressing noise in MRI data. The process between the noisy picture dataset and the residual image dataset is what constitutes residual learning. The cleaned picture dataset is then created from the residual learning output and subsequently processed using a multi-scale convolutional neural network. For further classification processing, this cleaned picture dataset is modified with smoothing, down sampling, and fine graining. Table 3 presents the list of recently published works related to enhancing the resolution of medical images while performing the various tasks using deep learning algorithms. In some works, autoencoders are used as image denoising algorithms, whereas few authors have reported modified deep learning algorithms used for denoising of the medical images before performing the classification, detection, or reconstruction-related tasks.