This study presents a complete quantum-aware Internet of Medical Things (IoMT) framework for secure medical imaging that integrates lightweight edge diagnostics, quantum key distribution, and hybrid image encryption. At the edge layer, MicroRadNet, an ultra-compact convolutional neural network (CNN) with exactly 3,140 trainable parameters, is deployed on a Raspberry Pi Zero 2 W, achieving 98.24% accuracy on chest X-ray classification with INT8 quantization. This reduces the model size to 3.14 KB and yields inference latency that meets real-time constraints. At the communication layer, a gateway-mediated BB84 protocol executed on Amazon Braket generates session keys via Golay [24, 12, 8] reconciliation (correcting up to t = 3 errors per codeword) and privacy amplification, producing 256-bit keys with quantum bit error rate (QBER) < 0.02. At the image-protection layer, a hybrid cipher combines 5D hyperchaotic permutation (seeded from BB84 key material), fixed-rule DNA encoding, and quantum-keyed diffusion. Security evaluation demonstrates near-ideal entropy, strong NPCR and UACI values, uniform ciphertext histogram distribution, low directional adjacent-pixel correlations, low average absolute correlation, large key space, high plaintext sensitivity, high key sensitivity, and stable chaotic-sequence randomness. These results provide empirical statistical validation of resistance to common statistical, differential, brute-force, and key-sensitivity attacks. End-to-end latency remains below the 1.5 s system constraint, validating practical edge deployment. The framework replaces classical public-key dependency with simulation-validated quantum-aware foundations while maintaining clinical accuracy and real-time performance, establishing a path toward deployable, standards-aligned quantum-aware healthcare security.

Knee Osteoarthritis (KOA) is a leading contributor to global physical disability, where delayed diagnosis often results in irreversible joint damage and socio-economic cost. Early diagnosis remains challenging due to subtle radiographic biomarkers and limited access to specialized expertise, particularly in distributed healthcare settings. Within the evolving landscape of the Future Internet, characterized by Internet of Medical Things (IoMT), edge–cloud computing, and intelligent digital health infrastructures, there is an increasing demand for scalable, low-latency, and explainable AI-driven diagnostic solutions. In this work, we propose a Dual-Stream Wavelet Fusion Network (DS-WFN) alongside a distributed edge-cloud architectural roadmap tailored for deployment in distributed and edge-enabled healthcare ecosystems. The framework integrates a spatial morphological stream with a spectral wavelet stream, augmented by an Adaptive Wavelet Selection Mechanism (AWSM). The AWSM dynamically selects optimal frequency bases (Haar, Symlet, Daubechies) to preserve fine-grained diagnostic features typically lost in conventional CNN architectures. An Adaptive Spatial Alignment (ASA) module further ensures efficient fusion of heterogeneous representations, enabling robust feature integration across computational nodes. Experimental results across a five-fold patient-isolated cross-validation protocol demonstrate that the DS-WFN achieves a mean classification accuracy of 76.3% (95% CI: 71.6–80.8%) and a macro-averaged F1-score of 0.747 (95% CI: 0.697–0.795), consistently outperforming single-stream baselines while preventing patient-level data leakage. Furthermore, Grad-CAM visualizations provide interpretable outputs aligned with clinical diagnostic criteria, supporting trustworthy AI integration into digital healthcare workflows. Furthermore, we disclose a methodological framework for edge-based implementation, highlighting how localized inference ensures data sovereignty and real-time clinical support. By combining multiscale signal processing with deep learning under a Future Internet paradigm, this work contributes a scalable, explainable, and edge-ready diagnostic framework for early KOA detection, enabling intelligent, connected, and resource-efficient healthcare services.