In the era of the Internet of Things (IoT), the transmission of medical reports in the form of scan images for collaborative diagnosis is vital for any telemedicine network. In this context, ensuring secure transmission and communication is necessary to protect medical data to maintain privacy. To address such privacy concerns and secure medical images against cyberattacks, this research presents a robust hybrid encryption framework that integrates quantum, and classical cryptographic methods. The proposed framework not only secure medical data against cyber threats but also protects the secret security keys. Initially, a Quantum Key Distribution (QKD) is employed to generate a shared key, which is then used to secure the symmetric keys via One-Time Pad (OTP) encryption. Next, bit-planes are extracted from each color component. The rows and columns of the extracted bit-planes are scrambled using random sequences which are generated by a 6D hyperchaotic Chen system and the Ikeda map. To further increase confusion in the original data, multiple-step pixel scrambling operations such as pixel shuffling, pixel value shuffling, and rotational and flipping operations are implemented. After the confusion phase, a combination of affine transformations with non-linear functions, Discrete Cosine Transform (DCT) with complex modulation, Discrete Wavelet Transform (DWT) with random phase modulation, bilinear transformation, and nonlinear polynomial mapping are employed to create diffusion in the scrambled components. These multiple encryption operations aim to maximize randomness in the final ciphertext image. Additionally, to reduce computational complexity, only the Most Significant Bit-Planes (MSBs) are encrypted, as they contain more than 94% of the plaintext information. Several experimental results and analyses are conducted to assess the proposed encryption framework, including entropy analysis, key sensitivity analysis, correlation analysis lossless analysis, and histogram analysis. Furthermore, the framework is tested against various cyberattacks such as brute-force attacks, clipping attacks, and noise attacks on the ciphertext images, to demonstrate its resilience against such threats.

As the internet of things (IoT) continues to proliferate, the need for efficient and secure data encryption has become increasingly critical, particularly for resource-constrained devices. Existing encryption methods offer adequate security for digital data; however, they often fall short when applied to resource-constrained IoT devices. This research introduces a novel lightweight encryption algorithm optimized with metaheuristic techniques, incorporating quantum encryption, confusion and diffusion operations, discrete wavelet transform (DWT), and multiple chaotic maps. Initially, a color image is decomposed into its three color components-red (R), green (G), and blue (B)-and then transformed into its quantum representation, where quantum encryption operations are performed. Following this, the quantum image is transformed back into a classical format to apply confusion and diffusion techniques. Confusion is achieved by generating a substitution matrix and applying a modular operation to introduce pixel-level confusion. A key matrix is then created to implement the diffusion operation. In the final phase, DWT is used to extract frequency sub-bands, forming a low-frequency sub-band and further extracting sub-bands up to the 4th level, which are substituted using values from the substitution box. The performance of the proposed encryption framework is evaluated through various statistical analyses, including entropy, correlation, key sensitivity, lossless analysis, and histogram analysis. The results demonstrate notable statistical measures with an entropy of 7.9998, a correlation of 0.0001, and a key space of \(2^{947.862}\). Additionally, the encryption’s robustness is tested against several cyberattacks, such as noise, cropping, and brute force, showcasing its effectiveness in resisting these threats.

Breast cancer remains one of the leading causes of cancer-related mortality among women worldwide. Early and accurate detection is critical for improving survival rates and enabling effective treatment. This research proposes a fine-tuned Multi-Deep Neural Network model with Multiple Datasets (FT-MDNNMDs). The proposed system employs a hierarchical classification strategy, first distinguishing cancer from normal cases, and subsequently classifying cancer-positive cases into Stage II and Stage III categories. The model integrates data from The Cancer Genome Atlas (TCGA) and a private clinical image dataset collected from hospitals in Pakistan. Preprocessing techniques, including image normalization, filtering, and Principal Component Analysis (PCA), are applied to enhance feature quality and reduce redundancy. Transfer learning and fine-tuning strategies are incorporated to further improve classification performance. The proposed model effectively distinguishes between benign and malignant tumors and accurately identifies stage II and stage III cases. Experimental results demonstrate that the fine-tuned MDNNMDs model achieves an accuracy of 99.57%, outperforming conventional machine learning algorithms such as Support Vector Machine (94.46%), Decision Tree (93.54%), and Naïve Bayes (91.22%). The model also achieved superior MCC (99.46%), F-score (99.57%), and recall (99.63%). In addition to algorithmic comparison, the performance of the proposed model is compared with existing breast cancer detection models, and it is revealed that the proposed model provides more accurate results than existing ones, with 1.5% to 3.0% better accuracy.