The rapid proliferation of multimodal misinformation on social media demands detection frameworks that are not only accurate but also robust to noise, adversarial manipulation, and semantic inconsistency between modalities. Existing multimodal fake news detection approaches often rely on deterministic fusion strategies, which limits their ability to model uncertainty and complex cross-modal dependencies. To address these challenges, we propose Q-ALIGNer, a quantum-inspired multimodal framework that integrates classical feature extraction with quantum state encoding, learnable cross-modal entanglement, and robustness-aware training objectives. The proposed framework adopts quantum formalism as a representational abstraction, enabling probabilistic modeling of multimodal alignment while remaining fully executable on classical hardware. Q-ALIGNer is evaluated on four widely used benchmark datasets—FakeNewsNet, Fakeddit, Weibo, and MediaEval VMU—covering diverse platforms, languages, and content characteristics. Experimental results demonstrate consistent performance improvements over strong text-only, vision-only, multimodal, and quantum-inspired baselines, including BERT, RoBERTa, XLNet, ResNet, EfficientNet, ViT, Multimodal-BERT, ViLBERT, and QEMF. Q-ALIGNer achieves accuracies of 91.2%, 92.9%, 91.7%, and 92.1% on FakeNewsNet, Fakeddit, Weibo, and MediaEval VMU, respectively, with F1-score gains of 3–4 percentage points over QEMF. Robustness evaluation shows a reduced adversarial accuracy gap of 2.6%, compared to 7%–9% for baseline models, while calibration analysis indicates improved reliability with an expected calibration error of 0.031. In addition, computational analysis shows that Q-ALIGNer reduces training time to 19.6 h compared to 48.2 h for QEMF at a comparable parameter scale. These results indicate that quantum-inspired alignment and entanglement can enhance robustness, uncertainty awareness, and efficiency in multimodal fake news detection, positioning Q-ALIGNer as a principled and practical content-centric framework for misinformation analysis.

Globally, diabetes and glaucoma account for a high number of people suffering from severe vision loss and blindness. To treat these vision disorders effectively, proper diagnosis must occur in a timely manner, and with conventional methods such as fundus photography, optical coherence tomography (OCT), and slit-lamp imaging, much depends on an expert’s interpretation of the images, making the systems very labor-intensive to operate. Moreover, clinical settings face difficulties with inter-observer variability and limited scalability with these diagnostic devices. To solve these problems, we have developed the Efficient Channel-Spatial Attention Network (ECSA-Net), a new deep learning-based methodology that integrates lightweight channel- and spatial-attention modules into a convolutional neural network. Ultimately, ECSA-Net improves the efficiency of computational resource use while enhancing discriminative feature extraction from retinal images. The ECSA-Net methodology was validated by conducting a series of classification accuracy tests using two publicly available eye disease datasets and was benchmark against a number of different pretrained convolutional neural network (CNN) architectures. The results showed that the ECSA-Net achieved classification accuracies of 60.00% and 69.92%, respectively, while using only a compact architecture with 0.56 million parameters. This represents a reduction in parameter size by a factor of 14 $\times$ to 247 $\times$ compared to other pretrained models. Additionally, the attention modules added to the architecture significantly increased sensitivity to disease-relevant regions of the retina while maintaining low computational cost, making ECSA-Net a viable option for real-time clinical use. ECSA-Net is both efficient and accurate in automating the classification of eye diseases, combining high performance with the ethical considerations of medical artificial intelligence (AI) deployment. The ECSA-Net framework mitigates algorithmic bias in training datasets and protects individuals’ privacy and transparency in decision-making, thereby facilitating human-AI collaboration. The two areas of technical performance and ethical integration are needed for the responsible and scalable use of ECSA-Net in a variety of ophthalmic care settings.