Medical image analysis technology based on deep learning has played an important role in computer-aided disease diagnosis and treatment. Classification accuracy has always been the primary goal pursued by researchers. However, the image transmission process also faces the problems of limited wireless ad-hoc network (WAN) bandwidth and increased security risks. Moreover, when user data are exposed to unauthorized users, platforms can easily leak personal privacy. Aiming at the abovementioned problems, a system model and an access control scheme for the collaborative analysis of the diagnosis of diabetic retinopathy (DR) are constructed in this paper. The system model includes two stages of data cleaning and lesion classification. In the data cleaning phase, the private cloud writes the model obtained after training into the blockchain, and other private clouds use the best-performing model on the chain to identify the image quality when cleaning data and pass the high-quality image to the lesion classification model for use. In the lesion classification stage, each private cloud trains the classification model separately; uploads its own model parameters to the public cloud for aggregation to obtain a global model; and then sends the global model to each private cloud to achieve collaborative learning, reduce the amount of data transmission, and protect personal privacy. Access control schemes include improved role-based access control (RAC) used within the private cloud and blockchain-based access control used during the interaction between the private cloud and the public cloud program (BAC). RAC grants both functional rights and data access rights to roles and takes into account object attributes for fine-grained level control. Based on certificateless public-key encryption technology and blockchain technology, BAC can realize the identity authentication and authority identification of the private cloud while requesting the transmission of model parameters from the private cloud to the public cloud and protect the security of the identity, authority, and model parameters of the private cloud to achieve the effect of lightweight access control. In the experimental part, two retinal datasets are used for DR classification analysis. The results show that data cleaning can effectively remove low-quality images and improve the accuracy of early lesion classification for doctors, with an accuracy rate of 90.2%.

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Heart Disease Dataset (HDD) contains high dimensions which poses challenges to research community in terms of complexity and efficient analysis. Heart disease is also called as cardiovascular disease (CVD). Feature selection will be made to reduce the irrelevant and redundant number of attributes. Fast diagnosis of the heart disease can be done using a knowledge driven approach. A comparison was made for medically important features to that of computerized subset of features, to bring out much simpler set of features used for the diagnosis. It focuses on the experts' judgement for medical driven feature selection process termed as MFS, and the performance of various classifiers on Cleveland dataset for the computerized feature selection termed as CFS and also a combination of both to enhance the prediction accuracy. Further, this paper categorizes the MFS, CFS and the combination of both into discrete and continuous sets of attributes. Our work has proved that the discrete features do not contribute much to the classification as do the continuous ones, in its accuracy, speed and performance.

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