{
  "id": 1569762,
  "title": "Explainable AD classification using integrated quantum-inspired deep neural and transformer models",
  "url": "https://urgent.news/2026/08/17/explainable-ad-classification-using-integrated-quantum-inspired-deep",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-17T00:00:00.000Z",
  "source": {
    "name": "Scientific Reports",
    "slug": "scientific-reports",
    "url": "https://www.nature.com/articles/s41598-026-63429-8"
  },
  "original_language": "en",
  "account": "A new study introduces an explainable and robust framework for classifying Alzheimer's disease (AD) using a combination of deep learning and quantum-inspired techniques. The proposed model, named Tri-Fusion-ADNet, integrates EfficientNet-V2-S for localized brain feature extraction, Swin TransformerV2 for modeling long-range contextual relationships, and a quantum-inspired variational neural network (QI-VNN) for enhanced higher-order feature interaction.\n\nThe researchers tested the model on four public brain MRI datasets containing various AD stages, including non-demented, mild cognitive impairment, and progressive Alzheimer's. Balanced and augmented scans were used, acquired with different imaging techniques and techniques, to account for heterogeneous neurodegenerative patterns and non-generalization issues across imaging sources.\n\nThe model underwent 10-fold cross-validation on multiple datasets to ensure reliability. Additionally, qualitative interpretability was achieved through Grad-CAM visualization. The results showed exceptional performance across all datasets, with testing accuracy ranging from 91.18% to 98.42%. Notably, when trained on one dataset and tested on another, the model maintained high accuracies of 91.22%, even with unseen-domain shifts, demonstrating its strong generalization capabilities.\n\nThis research, conducted at the Department of Computer Science and Engineering at Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology in India, was not funded by any specific grants. The findings were published under a Creative Commons Attribution 4.0 International License.",
  "summary": "Scientific Reports, Published online: 17 August 2026; doi:10.1038/s41598-026-63429-8 Explainable AD classification using integrated quantum-inspired deep neural and transformer models",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}