In recent times, security in cloud computing has become a significant part in healthcare services specifically in medical data storage and disease prediction. A large volume of data are produced in the healthcare environment day by day due to the development in the medical devices. Thus, cloud computing technology is utilised for storing, processing, and handling these large volumes of data in a highly secured manner from various attacks. This paper focuses on disease classification by utilising image processing with secured cloud computing environment using an extended zigzag image encryption scheme possessing a greater tolerance to different data attacks. Secondly, a fuzzy convolutional neural network (FCNN) algorithm is proposed for effective classification of images. The decrypted images are used for classification of cancer levels with different layers of training. After classification, the results are transferred to the concern doctors and patients for further treatment process. Here, the experimental process is carried out by utilising the standard dataset. The results from the experiment concluded that the proposed algorithm shows better performance than the other existing algorithms and can be effectively utilised for the medical image diagnosis.

中文翻译：

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基于卷积神经网络的基于云和物联网的医学图像诊断的安全性和保密性

近年来，云计算中的安全性已成为医疗保健服务中的重要组成部分，尤其是在医疗数据存储和疾病预测方面。由于医疗设备的发展，每天在保健环境中产生大量数据。因此，云计算技术被用于以高度安全的方式存储，处理和处理这些大量数据，以免受各种攻击。本文将重点放在疾病分类上，即利用具有扩展的之字形图像加密方案的安全云计算环境中的图像处理功能，该方案对不同的数据攻击具有更大的容忍性。其次，提出了一种模糊卷积神经网络（FCNN）算法对图像进行有效分类。解密后的图像用于通过不同的训练层对癌症水平进行分类。分类后，将结果转移给关注的医生和患者以进行进一步的治疗过程。在这里，实验过程是通过利用标准数据集进行的。实验结果表明，与现有算法相比，该算法具有更好的性能，可以有效地用于医学图像诊断。