Diagnosis of Alzheimer's disease from neuroimages using steerable quantum probabilistic hamiltonian generative...

Alzheimer's Disease (AD) is a disease of the brain that lowers quality of life due to cognitive impairment. A correct diagnosis is therefore essential for timely interventions and follow-up care for executives. However, existing deep learning methodologies for neuroimage-based diagnosis seriously lack reliability due to decreased accuracy and increased false positive rates, both of which can lead to misdiagnosis and suboptimal care planning. This study proposed an innovative Steerable Quantum Probabilistic Hamiltonian Generative Modeling with Adaptive Chaotic Satin Bowerbird Optimization (SQPHGM-ACSBO) framework for diagnosing AD using neuroimages within the ADNI dataset. A total of 5,154 neuroimages from the ADNI dataset were utilized in this study, comprising 2,590 MCI, 1,124 AD, and 1,440 cognitively normal (CN) samples. The process begins with image enhancement via an Adaptive Self-Guided Loop Filter, followed by precise brain region segmentation with GoogLeNet Inception-v3, and advanced feature extraction using a new Discrete Cosine-Krawtchouk-Tchebichef Transform (DCKTT). Classification is performed using the SQPHGM model, which combines the strengths of Steerable Transformers and Quantum-Probabilistic Hamiltonian Learning to model complex neuroimaging patterns, while the Adaptive Chaotic Satin Bowerbird Optimization (ACSBO) algorithm optimizes classification performance. With 99.9% accuracy and 99.8% precision, the suggested method outperforms current methods and provides a dependable, high-performance solution for AD diagnosis from neuroimaging data, according to experimental results. The main innovation of this work is the all-in-one optimization of steerable transformer-based directional feature learning, quantum-probabilistic Hamilton generator modeling, and adaptive-chaotic satin bowerbird optimization integrated within a single diagnostic pipeline, which allows for representing the complicated neuroimaging patterns better, and the convergence stability is also increased, compared to the previous deep learning-based Alzheimer diagnosis models.