## 1 Introduction
### Related Work
#### 1.1.1 Medical Image Classification  
Medical image classification, a subject integrating medical image technology and computer technology, has become a vital tool and technical means for medical research [13; 14; 15], disease diagnosis, and treatment. Recently, medical image classification based on deep learning [16] has become a hot research focus in the field of medical image research.  
[PERSON] et al. in [17] proposed an innovative representation learning framework for breast cancer diagnosis in mammography, which integrates deep learning technology to automatically identify features with the developed breast cancer benchmark dataset. The experimental results show that the proposed method has significantly improved the performance of the classification. [PERSON] et al. in [18] developed a graph-based semi-supervised learning (SSL) scheme using a deep convolutional neural network (CNN) for breast cancer diagnosis. The accuracy was 0.8243 with mixed labeled and unlabeled data. [PERSON] et al. in [19] extended the latest technology of CNN to help clinicians diagnose heart disease. It maintained the classification results of the eight view categories of echo video with 92.1% accuracy, while only a single spatial CNN network was used to achieve 89.5%. [PERSON] et al. in [20] presented a new partition CNN structure, which had two partition layers to label regions of interest in multidimensional hyperspectral images and classify them. They obtained a classification accuracy of 91.4% with a sensitivity of 0.94 and a specificity of 0.91 for 100 image data sets training. For task classification of a cancerous tumor, normal tissue accuracy of 94.5% for 500 training patterns were obtained. [PERSON] et al. in [21] proposed PPFDL, which is a joint privacy-preserving deep learning framework. Specifically, they designed a new solution to reduce the negative impact of irregular users on training accuracy. At the same time, Yao's garbled code circuit and homomorphic cryptosystem are used to ensure the confidentiality of all user-related information. A lot of experiments have proved its excellent performance in training accuracy, computation, and communication overhead.  
In real life, for hospitals and clinics with limited computing resources, it is difficult to train and classify medical images on the local side. Therefore, based on the above work, we have achieved the outsourcing of the medical image classification task to the cloud service platform so as to reduce the computing burden at the local level and improve the efficiency of the task with high accuracy. The above CNN applications in medical image classification are summarized in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**References** & **Architecture** & **Dataset** & **Performance Metric** & **Computation** & **Security** \\ \hline [PERSON] et al. [17] & (Conv, ReLU, maxpool) \(\times\) 2 + FC & Breast cancer & & AUC 82.2\% & Local side & \(\mathcal{K}\) \\ \hline [PERSON] et al. [18] & (Conv, maxpool) \(\times\) 3, FC & Breast cancer & & Accuracy 82.43\%, AUC 88.18\% & Local side & \(\mathcal{K}\) \\ \hline  & Two path CNN of seven & & & & \\  & layers (conv, ReLU, maxpool) \(\times\) 2, (conv, ReLU) \(\times\) 2, (conv, ReLU, maxpool) \(\times\) 2, (conv, dropout) & & & & \\  & & & & & \\ \hline [PERSON] et al. [20] & (Conv, ReLU, maxpool) \(\times\) 2, FC & & & & \\ \hline  & (Conv, ReLU), (Conv, ReLU, maxpool) \(\times\) 3, dropout, FC & Breast cancer & & & \\  & & & & & \\ \hline  & & & & & \\ \end{tabular}
\end{table}
Table 1: A summary of CNN applications in medical image classifcation.