The rise of digital medical imaging, like MRI and CT, demands strong encryption to protect patient data in telemedicine and cloud storage. Chaotic systems are popular for image encryption due to their sensitivity and unique characteristics, but existing methods often lack sufficient security. This paper presents the Three-dimensional Diffusion Algorithm and Deep Learning Image Encryption system (TDADL-IE), built on three key elements. First, we propose an enhanced chaotic generator using an LSTM network with a 1D-Sine Quadratic Chaotic Map (1D-SQCM) for better pseudorandom sequence generation. Next, a new three-dimensional diffusion algorithm (TDA) is applied to encrypt permuted images. TDADL-IE is versatile for images of any size. Experiments confirm its effectiveness against various security threats. The code is available at \href{https://github.com/QuincyQAQ/TDADL-IE}{https://github.com/QuincyQAQ/TDADL-IE}.

翻译：随着MRI和CT等数字医学成像技术的兴起，远程医疗和云存储中对患者数据的保护提出了强加密需求。混沌系统因其敏感性和独特特性在图像加密中备受青睐，但现有方法通常缺乏足够的安全性。本文提出了基于三个核心要素构建的三维扩散算法与深度学习图像加密系统（TDADL-IE）。首先，我们提出了一种增强型混沌生成器，它利用带有1D-Sine二次混沌映射（1D-SQCM）的LSTM网络来生成更优的伪随机序列。其次，采用一种新的三维扩散算法（TDA）对置乱后的图像进行加密。TDADL-IE适用于任意尺寸的图像。实验证实了其抵御多种安全威胁的有效性。代码可在 \href{https://github.com/QuincyQAQ/TDADL-IE}{https://github.com/QuincyQAQ/TDADL-IE} 获取。