<p>Since medical images are very sensitive and important for diagnosis and treatment, protecting their security and confidentiality is extremely important. There could be serious privacy and medical care issues if unlicensed personnel access or change such images. So, making sure medical images are transmitted and stored securely helps ensure private patient data is not compromised and healthcare systems maintain their reputation. It addresses these issues by suggesting a new entropy-based encryption system as a feature of the Mimic Octopus Adaptive Framework (MOAF). This method is made to securely save confidential data in the images of patients without changing their diagnostic usefulness or quality. Depending on the Shannon entropy present in the image data, MOAF opts for the most suitable compression algorithms (Vector Quantization or Huffman Coding) and suitable encryption algorithms (3DES or AES). Because of adaptive selection, each image gets its own third-tier security measure which boosts both safety and efficiency. Besides, a distinct cryptographic key is generated for each secret image using the SHA-256 hash function and the information from the extracted features and image entropy. Security and the process of decoding are improved by having the header appender directly embed vital data like the entropy, chosen algorithms and key into the header of the image. Evaluating the method experimentally reveals a peak signal-to-noise ratio (PSNR) of 81.31 and a structural similarity index measure (SSIM) of 1.0 at a payload capacity of 79,747 bytes. This proves that the quality of the image is extremely well preserved. It is shown through comparisons that MOAF works better than other techniques at being less detectable and stronger against various forms of attacks.</p>