# Deep Learning for Medical Image Cryptography: A Comprehensive Review
## 4 Deep Learning for Medical Image Cryptography
### Privacy-Preserving Object Detection and Classification in Medical Image Encryption Algorithms  
Deep learning-based analysis of medical images can extract sensitive information through object detection. These medical images include embedded personal information that must be secured. Consideration should be given to the security of the data itself as well as the security of the deep learning-based processing and the information that was collected. Given that it has been utilized and will be employed in several object-detection healthcare applications in the near future, the security issues with the deep learning-based approach becomes crucial. For medical image privacy, some research papers have reported on privacy-preserving object detection along with the image encryption algorithms. [PERSON] et al. (2014) have proposed the lightweight privacy-preserving faster R-CNN framework (SecRCNN) for object detection from medical images. While designing this framework, a secret sharing sub-protocol is added to complete the secure computation in it. To demonstrate the efficiency and secure computation through SecRCNN, comprehensive theoretical analysis is performed along with the extensive experiments. To improve the efficiency of SecRCNN, sub-protocols involved in this R-CNN are modified and authors are able to achieve better computation time and efficiency. [PERSON] et al. (2019) present fully homomorphic encryption tightly integrated with the CNN functions (CaRENets). They present a novel resource-efficient method for homomorphic inference on encrypted images. It has practical implications for secure deep learning inference in medical imaging systems. The authors have considered two ophthalmology applications involving the classification of retinopathy of prematurity (ROP) and diabetic \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref.** & **Year** & **Organ** & **Task** & \
\begin{tabular}{c} **Image** \\ **Modality** \\ \end{tabular} & **Datasets** & **Model** & **Metrics** &
\begin{tabular}{c} **Image** \\ **Denoising** \\ **Algorithm** \\ \end{tabular} \\ \hline
[108] & 2021 & Chest & Classification & X-ray & ChestX-Ray8 ([PERSON] et al., 2017) & DNN & PSNR, SSIM & DNN-based \\  & 2021 & Heart &
\begin{tabular}{c} Reconstruction \\ of high-quality \\ images \\ \end{tabular} & MRI scans & Stroke prediction ([PERSON] et al., 2018) & CNN & PSNR, SSIM, MSE & SARN technique \\  & & & & COVID-CT ([PERSON] et al., 2018) & Accuracy, \\
[123] & 2022 & Chest & Classification & CT scan, X-ray & Deep CNN & Precision, Recall, F1-score, Log and CT images ([PERSON] et al., 2018) & Autoencoder \\  & & & & NaF Prostate ([PERSON] et al., 2018) & Multi-scale & loss, PSNR, SSIM & \\
[124] & 2022 & Prostate gland & Detection & MRI scans & TCGA-PRAD ([PERSON] et al., 2018) & denoising & Sensitivity, \\  & & & & Prostate-3T ([PERSON] et al., 2018) & convolutional & Specificity, \\  & & & & PROSTATE- & neural network & Accuracy & learning \\
\begin{tabular}{c} **DIAGNOSIS ([PERSON] et al., 2018)** \\ \end{tabular} \\
[24] & 2023 & \begin{tabular}{c} Multi- \\ organ \\ \end{tabular} & Classification & \begin{tabular}{c} Real-time CT scans \\ \end{tabular} & Real time CT scans & Real time CT scans & \begin{tabular}{c} Pseudo-predictive \\ deep denoising \\ network \\ \end{tabular} & \
\begin{tabular}{c} SNR, SI, \\ CNR, ER \\ \end{tabular} &
\begin{tabular}{c} Deep denoising \\ network \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: List of medical images denoising techniques for deep learning-based medical image analysis. retinopathy (DR). They have provided the set of experiments to demonstrate that CaRENets can significantly improve both inference and memory efficiency. [PERSON] et al. [135] proposed an approach for a secure dyslexia biomarkers classification using a deep CNN model and residual number system (RNS) is used to encrypt the dataset. The proposed cascaded deep CNN shows classification outcomes with a performance accuracy of 73.2% on the encrypted data. [PERSON] et al.