Optimized Hybrid Deep Learning Framework for Early Detection of Alzheimer’s Disease Using Adaptive Weight Selection

Background: 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.
Methods: 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. Results: 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.Conclusions: 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.