Alarood Ala Abdulsalam, Faheem Muhammad, Al-Khasawneh Mahmoud Ahmad, Alzahrani Abdullah I A, Alshdadi Abdulrahman A
College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia.
School of Technology and Innovations University of Vaasa Vaasa Finland.
Healthc Technol Lett. 2023 Jul 19;10(4):87-98. doi: 10.1049/htl2.12049. eCollection 2023 Aug.
Recently, medical technologies have developed, and the diagnosis of diseases through medical images has become very important. Medical images often pass through the branches of the network from one end to the other. Hence, high-level security is required. Problems arise due to unauthorized use of data in the image. One of the methods used to secure data in the image is encryption, which is one of the most effective techniques in this field. Confusion and diffusion are the two main steps addressed here. The contribution here is the adaptation of the deep neural network by the weight that has the highest impact on the output, whether it is an intermediate output or a semi-final output in additional to a chaotic system that is not detectable using deep neural network algorithm. The colour and grayscale images were used in the proposed method by dividing the images according to the Region of Interest by the deep neural network algorithm. The algorithm was then used to generate random numbers to randomly create a chaotic system based on the replacement of columns and rows, and randomly distribute the pixels on the designated area. The proposed algorithm evaluated in several ways, and compared with the existing methods to prove the worth of the proposed method.
近年来，医疗技术不断发展，通过医学图像进行疾病诊断变得非常重要。医学图像通常会从网络的一端传输到另一端的各个分支。因此，需要高度的安全性。由于图像中的数据被未经授权使用而产生了问题。用于保护图像数据的方法之一是加密，这是该领域最有效的技术之一。混淆和扩散是这里涉及的两个主要步骤。这里的贡献在于通过对输出影响最大的权重来调整深度神经网络，无论该输出是中间输出还是半最终输出，此外还引入了一种使用深度神经网络算法无法检测到的混沌系统。在所提出的方法中，通过深度神经网络算法根据感兴趣区域对彩色和灰度图像进行划分。然后该算法用于生成随机数，基于列和行的替换随机创建一个混沌系统，并在指定区域随机分布像素。所提出的算法通过多种方式进行评估，并与现有方法进行比较，以证明所提方法的价值。
