The recovery of semantics from corrupted images is a significant challenge in image processing. Noise can obscure features, interfere with accurate analysis, and bias results. To address this issue, the Regularized Neighborhood Pixel Similarity Wavelet algorithm (PixSimWave) was developed for denoising Nifti (magnetic resonance imaging (MRI)). The PixSimWave algorithm uses regularized pixel similarity detection to improve the accuracy of noise reduction by creating patches to analyze the intensity of pixels and locate matching pixels, as well as adaptive neighborhood filtering to estimate noisy pixel values by allocating each pixel a weight based on its similarity. The wavelet transform breaks down the image into scales and orientations, allowing a sparse image representation to allocate a soft threshold on its similarity to the original pixels. The proposed method was evaluated on simulated and raw T1w MRIs, outperforming other methods in terms of an SSIM value of 0.9908 for a low Rician noise level of 3% and 0.9881 for a high noise level of 17%. The addition of Gaussian noise improved PSNR and SSIM, with the results indicating that the proposed method outperformed other models while preserving edges and textures. In summary, the PixSimWave algorithm is a viable noise-elimination approach that employs both sparse wavelet coefficients and regularized similarity with decreased computation time, improving the accuracy of noise reduction in images.

Alzheimer’s disease (AD) is the leading cause of dementia, affecting millions worldwide and characterized by progressive neuronal loss, brain atrophy, and cognitive decline. Early diagnosis of AD is crucial for improving treatment outcomes and prolonging patients' quality of life, as timely interventions at the earliest stages can slow disease progression and enhance survival rates. However, accurate early detection remains challenging due to overlapping features across disease stages and the complexity of neuroimaging data. Traditional diagnostic approaches and conventional machine learning techniques often struggle to capture subtle variations in early-stage AD, limiting their diagnostic efficacy. To address these limitations, we propose a novel deep learning framework that enhances the diagnostic performance of a pre-trained ResNet-50 model by integrating a Convolutional Block Attention Module (CBAM) and Multi-Head Self-Attention (MHSA). Our framework leverages attention mechanisms to refine feature representations at an early processing stage, enabling precise differentiation between disease stages. The integration of Cross-Attention further improves the model's ability to extract discriminative features from neuroimaging data, optimizing predictive performance. Trained and evaluated on the OASIS Kaggle and MIRIAD datasets, the model achieves state-of-the-art results, including an accuracy and AUC of 99.17% and 99.00% on the OASIS dataset, and an accuracy of 98.59% and AUC of 98.44% on the MIRIAD dataset. Its near-perfect performance across all evaluated metrics establishes its potential as a transformative tool for early AD diagnosis.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder with increasing prevalence among the aging population, necessitating early and accurate diagnosis for effective disease management. In this study, we present a novel hybrid deep learning framework that integrates both 2D Convolutional Neural Networks (2D-CNN) and 3D Convolutional Neural Networks (3D-CNN), along with a custom loss function and volumetric data augmentation, to enhance feature extraction and improve classification performance in AD diagnosis. According to extensive experiments, AlzhiNet outperforms standalone 2D and 3D models, highlighting the importance of combining these complementary representations of data. The depth and quality of 3D volumes derived from the augmented 2D slices also significantly influence the model's performance. The results indicate that carefully selecting weighting factors in hybrid predictions is imperative for achieving optimal results. Our framework has been validated on the Magnetic Resonance Imaging (MRI) from Kaggle and MIRIAD datasets, obtaining accuracies of 98.9% and 99.99%, respectively, with an AUC of 100%. Furthermore, AlzhiNet was studied under a variety of perturbation scenarios on the Alzheimer's Kaggle dataset, including Gaussian noise, brightness, contrast, salt and pepper noise, color jitter, and occlusion. The results obtained show that AlzhiNet is more robust to perturbations than ResNet-18, making it an excellent choice for real-world applications. This approach represents a promising advancement in the early diagnosis and treatment planning for Alzheimer's disease.