Gasmi Karim, Alyami Abdulrahman, Hamid Omer, Altaieb Mohamed O, Shahin Osama Rezk, Ben Ammar Lassaad, Chouaib Hassen, Shehab Abdulaziz
Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia.
Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia.
Diagnostics (Basel). 2024 Dec 11;14(24):2779. doi: 10.3390/diagnostics14242779.
Alzheimer's disease (AD) is a progressive neurological disorder that significantly affects middle-aged and elderly adults, leading to cognitive deterioration and hindering daily activities. Notwithstanding progress, conventional diagnostic techniques continue to be susceptible to inaccuracies and inefficiencies. Timely and precise diagnosis is essential for early intervention.
We present an enhanced hybrid deep learning framework that amalgamates the EfficientNetV2B3 with Inception-ResNetV2 models. The models were integrated using an adaptive weight selection process informed by the Cuckoo Search optimization algorithm. The procedure commences with the pre-processing of neuroimaging data to guarantee quality and uniformity. Features are subsequently retrieved from the neuroimaging data by utilizing the EfficientNetV2B3 and Inception-ResNetV2 models. The Cuckoo Search algorithm allocates weights to various models dynamically, contingent upon their efficacy in particular diagnostic tasks. The framework achieves balanced usage of the distinct characteristics of both models through the iterative optimization of the weight configuration. This method improves classification accuracy, especially for early-stage Alzheimer's disease. A thorough assessment was conducted on extensive neuroimaging datasets to verify the framework's efficacy.
The framework attained a Scott's Pi agreement score of 0.9907, indicating exceptional diagnostic accuracy and dependability, especially in identifying the early stages of Alzheimer's disease. The results show its superiority over current state-of-the-art techniques.
The results indicate the substantial potential of the proposed framework as a reliable and scalable instrument for the identification of Alzheimer's disease. This method effectively mitigates the shortcomings of conventional diagnostic techniques and current deep learning algorithms by utilizing the complementing capabilities of EfficientNetV2B3 and Inception-ResNetV2 by using an optimized weight selection mechanism. The adaptive characteristics of the Cuckoo Search optimization facilitate its application across many diagnostic circumstances, hence extending its utility to a wider array of neuroimaging datasets. The capacity to accurately identify early-stage Alzheimer's disease is essential for facilitating prompt therapies, which are crucial for decelerating disease development and enhancing patient outcomes.
阿尔茨海默病（AD）是一种进行性神经疾病，对中老年人有显著影响，导致认知功能衰退并妨碍日常活动。尽管取得了进展，但传统诊断技术仍然容易出现不准确和效率低下的问题。及时准确的诊断对于早期干预至关重要。
我们提出了一种增强的混合深度学习框架，该框架将EfficientNetV2B3与Inception-ResNetV2模型合并。这些模型通过基于布谷鸟搜索优化算法的自适应权重选择过程进行集成。该过程从神经影像数据的预处理开始，以确保质量和一致性。随后利用EfficientNetV2B3和Inception-ResNetV2模型从神经影像数据中提取特征。布谷鸟搜索算法根据各个模型在特定诊断任务中的功效动态地为它们分配权重。该框架通过权重配置的迭代优化实现了对两种模型不同特征的平衡使用。这种方法提高了分类准确率，尤其是对于早期阿尔茨海默病。对大量神经影像数据集进行了全面评估，以验证该框架的有效性。
该框架获得了0.9907的斯科特Pi一致性分数，表明其具有卓越的诊断准确性和可靠性，尤其是在识别阿尔茨海默病的早期阶段。结果显示了其相对于当前最先进技术的优越性。
结果表明所提出的框架作为一种可靠且可扩展的阿尔茨海默病识别工具具有巨大潜力。该方法通过使用优化的权重选择机制，利用EfficientNetV2B3和Inception-ResNetV2的互补能力，有效缓解了传统诊断技术和当前深度学习算法的缺点。布谷鸟搜索优化的自适应特性便于其在多种诊断情况下应用，从而将其效用扩展到更广泛的神经影像数据集。准确识别早期阿尔茨海默病的能力对于促进及时治疗至关重要，而及时治疗对于减缓疾病发展和改善患者预后至关重要。
