Introduction: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, memory loss, and impaired daily functioning. Despite significant research, AD remains incurable, highlighting the critical need for early diagnosis and intervention to improve patient outcomes. Timely detection plays a crucial role in managing the disease more effectively. Pretrained convolutional neural networks (CNNs) trained on large-scale datasets, such as ImageNet, have been employed for AD classification, providing a head start for developing more accurate models.Methods: This paper proposes a novel hybrid deep learning approach that combines the strengths of two specific pretrained architectures. The proposed model enhances the representation of AD-related patterns by leveraging the feature extraction capabilities of both networks. We validated this model using a large dataset of MRI images from AD patients. Performance was evaluated in terms of classification accuracy and robustness against noise, and the results were compared to several commonly used models in AD detection.Results: The proposed hybrid model demonstrated significant performance improvements over individual models, achieving an accuracy classification rate of 99.85%. Comparative analysis with other models further revealed the superiority of the new architecture, particularly in terms of classification rate and resistance to noise interference.Discussion: The high accuracy and robustness of the proposed hybrid model suggest its potential utility in early AD detection. By improving feature representation through the combination of two pretrained networks, this model could provide clinicians with a more reliable tool for early diagnosis and monitoring of AD progression. This approach holds promise for aiding in timely diagnoses and treatment decisions, contributing to better management of Alzheimer’s disease.

Pneumonia remains a significant global health threat, especially among children, the elderly, and immunocompromised individuals. Chest X-ray (CXR) imaging is commonly used for diagnosis, but manual interpretation is prone to errors and variability. To address these challenges, we propose a novel attention-guided deep learning framework that combines spatial, temporal, and biologically inspired processing for robust and interpretable pneumonia detection. Our method integrates convolutional operations for spatial feature extraction, gated recurrent mechanisms to capture temporal dependencies, and spike-based neural processing to mimic biological efficiency and improve noise tolerance. The inclusion of an attention mechanism enhances the model’s interpretability by identifying clinically relevant regions within the images. We evaluated the proposed method on a publicly available CXR dataset, achieving a high accuracy of 99.35%, along with strong precision, recall, and F1-score. Extensive experiments demonstrate the model’s robustness to various types of image distortions, including Gaussian blur, salt-and-pepper noise, and speckle noise. These results confirm the effectiveness, reliability, and transparency of the proposed approach, making it a promising tool for clinical deployment, particularly in low-resource healthcare environments.

Early and accurate diagnosis of Alzheimer’s disease (AD) is crucial for effective treatment. While the integration of deep learning techniques for AD classification is not entirely new, this study introduces CAPCBAM—a framework that extends prior approaches by combining Capsule Networks with a Convolutional Block Attention Module (CBAM). In CAPCBAM, standardized preprocessing of MRI images is followed by feature extraction using Capsule Networks, which preserve spatial hierarchies and capture intricate relationships among image features. The subsequent application of CBAM, employing both channel and spatial attention mechanisms, refines the feature maps to highlight the most clinically relevant regions. This dual-attention strategy offers clear advantages over conventional CNN methods, particularly in enhancing model generalization and mitigating information loss due to pooling. On the ADNI dataset, CAPCBAM achieved an impressive accuracy of 99.95%, with precision and recall both at 99.8%, an AUC of 0.99, and an F1-Score of 99.92%. Although the use of Capsule Networks and attention mechanisms has been explored previously, CAPCBAM distinguishes itself by its robust integration of these components. The study’s advantages include improved feature extraction, faster convergence, and superior classification performance, making it a promising tool for the early detection of Alzheimer’s disease.

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and structural brain alterations such as cortical atrophy and hippocampal degeneration. Early diagnosis remains challenging due to subtle neuroanatomical changes in early stages. This study proposes a hybrid convolutional neural network-spiking neural network (CNN-SNN) architecture to classify AD stages using structural MRI (sMRI) data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The model synergizes CNNs for hierarchical spatial feature extraction and SNNs for biologically inspired temporal dynamics processing. The CNN component processes image slices through convolutional layers, batch normalization, and dropout, while the SNN employs leaky integrate-and-fire (LIF) neurons across 25 time steps to simulate temporal progression of neurodegeneration—even with static sMRI inputs. Trained on a three-class task [AD, mild cognitive impairment (MCI), and cognitively normal (CN) subjects], the hybrid network optimizes mean squared error (MSE) loss with L2 regularization and Adam, incorporating early stopping to enhance generalization. Evaluation on ADNI data demonstrates robust performance, with training/validation accuracy and loss tracked over 30 epochs. Classification metrics (precision, recall, F₁-score) highlight the model’s ability to disentangle complex spatiotemporal patterns in neurodegeneration. Visualization of learning curves further validates stability during training. An ablation study demonstrates the SNN’s critical role, with its removal reducing accuracy from 99.58 to 75.67%, underscoring the temporal module’s importance. The SNN introduces architectural sparsity via spike-based computation, reducing overfitting and enhancing generalization while aligning with neuromorphic principles for energy-efficient deployment. By bridging deep learning with neuromorphic principles, this work advances AD diagnostic frameworks, offering a computationally efficient and biologically plausible approach for clinical neuroimaging. The results underscore the potential of hybrid CNN-SNN architectures to improve early detection and stratification of neurodegenerative diseases, paving the way for future applications in neuromorphic healthcare systems.

We propose a novel hybrid deep learning framework that synergistically integrates Convolutional Neural Networks (CNNs), Spiking Neural Networks (SNNs), and Generative Adversarial Networks (GANs) for robust and accurate classification of high-resolution frontal and sagittal human gait video sequences—capturing both lower-limb kinematics and upper-body posture—from subjects with Knee Osteoarthritis (KOA), Parkinson’s Disease (PD), and healthy Normal (NM) controls, classified into three disease-type categories. Our approach first employs a tailored CNN backbone to extract rich spatial features from fixed-length clips (e.g., 16 frames resized to 128 × 128 px), which are then temporally encoded and processed by an SNN layer to capture dynamic gait patterns. To address class imbalance and enhance generalization, a conditional GAN augments rare severity classes with realistic synthetic gait sequences. Evaluated on the controlled, marker-based KOA-PD-NM laboratory public dataset, our model achieves an overall accuracy of 99.47%, a sensitivity of 98.4%, a specificity of 99.0%, and an F1-score of 98.6%, outperforming baseline CNN, SNN, and CNN–SNN configurations by over 2.5% in accuracy and 3.1% in F1-score. Ablation studies confirm that GAN-based augmentation yields a 1.9% accuracy gain, while the SNN layer provides critical temporal robustness. Our findings demonstrate that this CNN–SNN–GAN paradigm offers a powerful, computationally efficient solution for high-precision, gait-based disease classification, achieving a 48.4% reduction in FLOPs (1.82 GFLOPs to 0.94 GFLOPs) and 9.2% lower average power consumption (68.4 W to 62.1 W) on Kaggle P100 GPU compared to CNN-only baselines. The hybrid model demonstrates significant potential for energy savings on neuromorphic hardware, with an estimated 13.2% reduction in energy per inference based on FLOP-based analysis, positioning it favorably for deployment in resource-constrained clinical environments and edge computing scenarios.