# Deep Learning for Medical Image Cryptography: A Comprehensive Review
## 4 Deep Learning for Medical Image Cryptography
### Encryption Algorithms for Deep Learning-Based Medical Image Analysis  
Medical imaging is regarded as one of the most sensitive and significant types of data in information systems. A robust encryption scheme that can withstand adversarial or cryptographic attacks is necessary when sending medical images over the network. Confidentiality is the most crucial component that requires careful consideration among the three security objectives for the security of information systems, namely confidentiality, integrity, and availability. When it comes to the encryption of medical images while they are processed by deep learning algorithms, there are two types of techniques, namely cryptography and homomorphic encryption, that are commonly documented in the literature. Here, some of the most recent work in this direction has been discussed. Radiologists could utilize the technique that [PERSON] et al. [112] demonstrated to identify lung diseases. The DenseNet-121 model is used by the suggested system to process the chest X-ray pictures. Additionally, they used the AES-128 technique to secure the private data that are included in medical photographs before uploading them to the cloud. An online cloud program has been created by [PERSON] et al. [113] to assess if tumors are visible in MRI images. The photos were sent to the cloud server using the AES-256 cryptographic technique. For tumor inference from the images, they utilized the CNN model which had an accuracy rate of 97.87%. The smart healthcare system presented by [PERSON] et al. [114] incorporates the capability of tumor detection from brain MRI images using CNN-LSTM and additionally the proposed system is secured by using the SHA-256 encryption technique. In the suggested smart healthcare system, the modified SHA-256 algorithm is recommended for safe medical data processing. With the suggested secure medical data processing system, they may attain the accuracy of 98.51%. [PERSON] et al. [115] presented a strategy based on an LSTM model that is trained on the MIMIC-III dataset and uses homomorphic encryption to protect sensitive data. Because the CKKS encryption system can be used to time series data, such as hourly clinical statistics, homomorphic encryption is used instead. For the goal of predicting in-hospital mortality, they have deployed this encrypted LSTM model with enhanced accuracy. PINPOINT, a temporal convolutional neural network, was developed by [PERSON] et al. [116] to predict time series while maintaining anonymity. It is suggested to use a cloud-based forecasting system with integrated homomorphic encryption and temporal CNN. The suggested system is validated using four different kinds of datasets. Forecasting of new cases for COVID-19 is also included, and it is successfully carried out with positive results. [PERSON] et al. [111] presented the systematic review on homomorphic encryption and its applications in the healthcare sector. They have discussed homomorphic encryption evolution and their types along with the applications. In this paper, authors have discussed and compared various approaches using homomorphic encryption for detecting cancer, analyzing heart rate, cardiovascular problems and also secure query generating systems in healthcare. The privacy-preserving deep learning strategy for categorizing COVID-19 from the chest X-ray pictures was presented by [PERSON] et al. [107]. In order to classify COVID-19 X-ray images, the COVID-19 dataset is encrypted using partly homomorphic encryption, and the encrypted images are then fed into the MobileNetV2 model. When they use a deep learning model on encrypted photos, they can obtain an accuracy of 93.3%. Table 2 lists the current encryption techniques for deep learning-based medical image processing. When conducting activities relating to disease detection, classification, segmentation, and COVID-19 forecasting, these encryption algorithms are also used. While researchers have just lately begun to report on homomorphic encryption, AES and SHA are the two most often used cryptographic algorithms.