Prediction of Alzheimer Disease using LeNet-CNN model with Optimal Adaptive Bilateral Filtering

Alzheimer's disease is a kind of degenerative dementia that causes progressively worsening memory loss and other cognitive physical impairments over time. Mini-Mental State Examinations screening tools are helpful for early detection, but diagnostic MRI brain analysis required. When (AD) detected in its earliest stages, patients may begin protective treatments before permanent damage has occurred. The characteristics the lesion sites AD affected role, as identified by MRI, exhibit great variety dispersed across image space, demonstrated cross-sectional imaging investigations disease. Optimized Adaptive Bilateral filtering using deep learning model was suggested part this study's approach toward end. Denoising pictures with help adaptive bilateral filter first stage (ABF). ABF improves denoising edge, detail, homogenous areas separately. After then, given weight, Equilibrium Optimizer used to determine best possible value weight (AEO). LeNet, CNN model, then complete organization. step LeNet-5 network identify study model's structure parameters. ADNI experimental dataset verify technique compare it models. findings prove method can achieve classification accuracy 97.43%, 98.09% specificity, 97.12% sensitivity, 89.67% Kappa index. compared against competing algorithms, emerges victorious.