Quantum Infused ANN Architecture for Diagnostic Safe Image Reduction

This project presented a Quantum‑Infused ANN Architecture for Diagnostic‑Safe Image Reduction, aimed at addressing the growing need for efficient storage and transmission of medical images. With the increasing use of digital imaging technologies in healthcare, managing large volumes of medical image data has become a significant challenge. The proposed system was designed to reduce image size while preserving the quality and important diagnostic details required for medical analysis. The developed model combines quantum-inspired feature extraction techniques with an Autoencoder-based Artificial Neural Network to achieve effective image compression. The preprocessing, feature extraction, compression, and reconstruction stages worked together to minimize redundant information while maintaining the essential characteristics of medical images. The reconstructed images retained most of the important visual and structural details, making them suitable for healthcare applications. The performance of the system was evaluated using parameters such as Compression Ratio (CR), Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), and Structural Similarity Index (SSIM). The results showed that the proposed approach achieved efficient compression with minimal loss of image quality. The model was able to reduce storage requirements and improve transmission efficiency while maintaining the reliability of medical image data. In conclusion, the proposed Quantum-Enhanced Artificial Neural Network model provides an effective and intelligent solution for medical image compression. It demonstrates how advanced technologies such as Artificial Intelligence and quantum-inspired computing can be utilized to improve healthcare data management. Future enhancements may include the use of real quantum computing platforms, cloud-based medical image storage, and more advanced deep learning techniques to further improve compression performance and support large-scale healthcare systems.

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