Alasiry Areej, Shinan Khlood, Alsadhan Abeer Abdullah, Alhazmi Hanan E, Alanazi Fatmah, Ashraf M Usman, Muhammad Taseer
Department of Informatics and Computer Systems, College of Computer Science, King Khalid University, Abha, Saudi Arabia.
Department of Computers, College of Engineering and Computers in Al-Lith, Umm Al-Qura University, Makkah, Saudi Arabia.
Sci Rep. 2025 Jul 2;15(1):23011. doi: 10.1038/s41598-025-05529-5.
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that significantly impacts cognitive function, posing a major global health challenge. Despite its rising prevalence, particularly in low and middle-income countries, early diagnosis remains inadequate, with projections estimating over 55 million affected individuals by 2022, expected to triple by 2050. Accurate early detection is critical for effective intervention. This study presents Neuroimaging-based Early Detection of Alzheimer's Disease using Deep Learning (NEDA-DL), a novel computer-aided diagnostic (CAD) framework leveraging a hybrid ResNet-50 and AlexNet architecture optimized with CUDA-based parallel processing. The proposed deep learning model processes MRI and PET neuroimaging data, utilizing depthwise separable convolutions to enhance computational efficiency. Performance evaluation using key metrics including accuracy, sensitivity, specificity, and F1-score demonstrates state-of-the-art classification performance, with the Softmax classifier achieving 99.87% accuracy. Comparative analyses further validate the superiority of NEDA-DL over existing methods. By integrating structural and functional neuroimaging insights, this approach enhances diagnostic precision and supports clinical decision-making in Alzheimer's disease detection.
阿尔茨海默病（AD）是一种进行性神经退行性疾病，对认知功能有重大影响，是一项重大的全球健康挑战。尽管其患病率不断上升，尤其是在低收入和中等收入国家，但早期诊断仍然不足，据预测，到2022年受影响的个体将超过5500万，预计到2050年将增至三倍。准确的早期检测对于有效干预至关重要。本研究提出了基于神经影像学的深度学习阿尔茨海默病早期检测（NEDA-DL），这是一种新颖的计算机辅助诊断（CAD）框架，利用混合ResNet-50和AlexNet架构，并通过基于CUDA的并行处理进行了优化。所提出的深度学习模型处理MRI和PET神经影像数据，利用深度可分离卷积提高计算效率。使用包括准确率、灵敏度、特异性和F1分数在内的关键指标进行性能评估，结果显示其具有先进的分类性能，Softmax分类器的准确率达到99.87%。对比分析进一步验证了NEDA-DL相对于现有方法的优越性。通过整合结构和功能神经影像学见解，该方法提高了诊断精度，并为阿尔茨海默病检测中的临床决策提供支持。
