In this paper, a new color watermarking algorithm based on differential evolution is proposed. A color host image is first converted from RGB space to YIQ space, which is more suitable for the human visual system. Then, apply three-level discrete wavelet transformation to luminance component Y and generate four different frequency sub-bands. After that, perform singular value decomposition on these sub-bands. In the watermark embedding process, apply discrete wavelet transformation to a watermark image after the scrambling encryption processing. Our new algorithm uses differential evolution algorithm with adaptive optimization to choose the right scaling factors. Experimental results show that the proposed algorithm has a better performance in terms of invisibility and robustness.

Parkinson’s disease (PD) is one of the common neurodegenerative diseases of the elderly. However, modern healthcare lacks the apparatus to detect the early signs of the disease, with only selected experts being able to spot the onset. Therefore, the early detection of PD is particularly important. Convolutional neural networks, a deep learning technique that can automatically extract image features, have been widely used in the diagnosis of medical images. Due to the complexity of the organization in the brain, we proposed a multiscale hybrid attention network (MSHANet) for the automatic detection of healthy and PD patients. MSHANet consisted of designed multiscale convolutional blocks and introduced hybrid attention blocks, so it can capture complex features in brain images. Two datasets were created using the images in the publicly available Parkinson’s progression markers initiative (PPMI) dataset, where the SV_3Dataset consisted of axial slices located in the substantia nigra region, and the MV_3Dataset adds mid-sagittal slices and striatal slices based on SV_3Dataset. For these two datasets, we proposed two different classification strategies, namely, parallel network classification (PNC) and multislice fusion classification (MSFC), to improve the classification performance of PD. After cross-validation experiments, the best results for the model using the PNC strategy achieved are 90.59% of accuracy, 90.59% of precision, 90.61% of recall, 90.6% of F1 score, and 0.956 of area under the curve (AUC). By analyzing the above results, the striatal slice in MV_3Dataset provides higher accuracy than the other two slices. Both PNC and MSFC improved the classification effect of MSHANet on PD and healthy control (HC), and the effect of PNC was better. The PNC strategy is used to test the performance of MSHANet on the test set. The best result is that the accuracy rate is 94.11%, the accuracy rate is 94.18%, the recall rate is 94.16, the F1 value is 94.17%, and the AUC is 0.9585. Our proposed method can help clinicians in accurately diagnosing the PD.

Computer-aided diagnosis (CAD) plays a crucial role in the clinical application of Alzheimer’s disease (AD). In particular, convolutional neural network (CNN)-based methods are highly sensitive to subtle changes caused by brain atrophy in medical images (e.g., magnetic resonance imaging, MRI). Due to computational resource constraints, most CAD methods focus on quantitative features in specific regions, neglecting the holistic nature of the images, which poses a challenge for a comprehensive understanding of pathological changes in AD. To address this issue, we propose a lightweight dual multi-level hybrid pyramid convolutional neural network (DMA-HPCNet) to aid clinical diagnosis of AD. Specifically, we introduced ResNet as the backbone network and modularly extended the hybrid pyramid convolution (HPC) block and the dual multi-level attention (DMA) module. Among them, the HPC block is designed to enhance the acquisition of information at different scales, and the DMA module is proposed to sequentially extract different local and global representations from the channel and spatial domains. Our proposed DMA-HPCNet method was evaluated on baseline MRI slices of 443 subjects from the ADNI dataset. Experimental results show that our proposed DMA-HPCNet model performs efficiently in AD-related classification tasks with low computational cost.

The pathogenesis of Alzheimer's disease (AD) is extremely intricate, which makes AD patients almost incurable. Recent studies have demonstrated that analyzing multi-modal data can offer a comprehensive perspective on the different stages of AD progression, which is beneficial for early diagnosis of AD. In this paper, we propose a deep self-reconstruction fusion similarity hashing (DS-FSH) method to effectively capture the AD-related biomarkers from the multi-modal data and leverage them to diagnose AD. Given that most existing methods ignore the topological structure of the data, a deep self-reconstruction model based on random walk graph regularization is designed to reconstruct the multi-modal data, thereby learning the nonlinear relationship between samples. Additionally, a fused similarity hash based on anchor graph is proposed to generate discriminative binary hash codes for multi-modal reconstructed data. This allows sample fused similarity to be effectively modeled by a fusion similarity matrix based on anchor graph while modal correlation can be approximated by Hamming distance. Especially, extracted features from the multi-modal data are classified using deep sparse autoencoders classifier. Finally, experiments conduct on the AD Neuroimaging Initiative database show that DS-FSH outperforms comparable methods of AD classification. To conclude, DS-FSH identifies multi-modal features closely associated with AD, which are expected to contribute significantly to understanding of the pathogenesis of AD.