Spatial Attention-Based Adaptive CNN Model for Differentiating Dementia with Lewy Bodies and Alzheimer's Disease

Differentiation of Alzheimer's Disease (AD) and Dementia with Lewy Bodies (DLB) utilizing brain perfusion Single Photon Emission Tomography (SPECT) is crucial and it might be difficult to distinguish between the two illnesses. The most recently discovered characteristic of DLB for a possible diagnosis is the Cingulate Island Sign (CIS). This work aims to differentiate DLB and AD by utilizing a deep learning model and this model is named AD-DLB-DNet. Initially, the required images are collected from the benchmark dataset. Further, the Spatial Attention-Based Adaptive Convolution Neural Network (SA-ACNN) is used to visualize the CIS features from the images where the attributes are tuned using Improved Random Function-based Birds Foraging Search (IRF-BFS). Further, CIS features attained from the SA-ACNN are used to accurately differentiate the DLB and AD. Finally, the Dilated Residual-Long Short-Term Memory (DR-LSTM) layer is proposed to accurately perform the AD and DLB differentiation for identifying the clinical characteristics of the DLB. The suggested model is used for differentiating between AD and DLB for taking effective therapeutic measures. Finally, the validation is performed to validate the effectiveness of the introduced system.

@article{Sravani2025,
  title     = {Spatial Attention-Based Adaptive CNN Model for Differentiating Dementia with Lewy Bodies and Alzheimer's Disease},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {2},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {K Sravani and V RaviSankar},
  doi       = {10.14569/IJACSA.2025.0160254},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160254}
}