This project proposes a secure and efficient method for transmitting sensitive medical data through a Lightweight DWT (Discrete Wavelet Transform) Steganography framework combined with ECC (Elliptic Curve Cryptography) and ChaCha20 encryption. In the realm of medical systems, the confidentiality and integrity of diagnostic data, such as medical images and patient records, are paramount. However, traditional cryptographic solutions like RSA and AES, while secure, impose significant computational overheads, making them unsuitable for real-time, resource-constrained environments. To address these limitations, the proposed system integrates DWT-based steganography, which embeds encrypted data within medical images, ensuring that the data remains imperceptible to human observers while maintaining the image's diagnostic quality. The encryption layer employs ChaCha20, a lightweight and high-performance stream cipher, alongside ECC for efficient key exchange, offering a balance of security and computational efficiency ideal for environments with limited resources. The proposed stego-crypto framework is optimized for real-time applications, ensuring minimal power consumption, low memory usage, and high resilience against common image perturbations such as noise, compression, and geometric transformations. The system's effectiveness is evaluated through various performance metrics, including PSNR (Peak Signal-to-Noise Ratio), MSE (Mean Squared Error), SSIM (Structural Similarity Index), and Bit Error Rate (BER), demonstrating superior performance compared to traditional cryptographic methods. This framework is ideal for secure medical data transmission in scenarios where both security and resource efficiency are critical, ensuring that sensitive patient information is protected while minimizing computational overhead.

Keywords: DWT, Steganography, ECC, ChaCha20, Medical Systems, Data Security, Image Processing, Secure Transmission, Low-Power Encryption, Computational Efficiency.