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Medical images can be constructed in two-dimensional (2D) or three-dimensional (3D) view imaging to be applied in disease detection and diagnosis inside the body, such as cancer/tumor, heart, or lung-related diseases. These images may contain patients&#x2019; privacy information and clinical diagnosis records. Hence, these images need to ensure authorization demands among hospitals, medical service organizations, or physicians in a picture archiving and communication system. This study presents an intelligent symmetric cryptography with a chaotic map and quantum-based key generator (KG) for medical image encryption and decryption. Overall scheme processes include (1) random cipher code generation, (2) training gray relational analysis (GRA)-based encryptor and decryptor, and (3) decrypted image evaluation. The hybrid chaotic map and quantum-based KG are used to increase the chaotic complexity and unpredictable levels to produce cipher codes for changing pixel values (substitution method) in a 2D image by 256 key-space cipher codes. The first and second GRA models are used to train the cipher codes to achieve an encryptor and a decryptor, respectively. Through the methodology validation using a chest X-ray database, the structural similarity index measurement is employed to evaluate the decryption quality between the plain image and decrypted image. The encrypted images show a visual uncorrelation with the plain images, and experimental results indicate higher confidences against the passive eavesdropper.

Intelligent Symmetric Cryptography With Chaotic Map and Quantum Based Key Generator for Medical Images Infosecurity