Horned Lizard Optimization-Driven CA-InceptionResNet Framework for Enhanced Dementia Diagnosis

Abstract

Dementia is a progressive neurological disorder affecting millions worldwide. It places a tremendous burden on global healthcare systems and research. Early detection and accurate diagnosis are essential for effective disease management. The purpose of this paper is to present an integrated framework using Deep Learning (DL) to help improve dementia diagnosis. The framework uses an Adaptive Difference of Gaussian (ADoG) filter to sharpen important edges on neuroimaging scans while reducing background noise, producing sharper (more focused) data in neuroimaging scans that contain significant amounts of data. To segment brain tissue into anatomical regions affected by neurodegenerative disorders using the Hybrid Agglomerative Clustering Refinement (HACR) technique. The segmented anatomical regions are analyzed using a polar-coordinate-based Gray-Level Co-Occurrence Matrix (GLCM) to extract rotation-invariant texture features, ensuring robust and orientation-independent characterization of tissue patterns. For a classification stage, proposed framework utilizes a Channel-Attention Inception-ResNet (CA-InceptionResNet) model. By integrating feature extraction stength of InceptionResNet architecture with channel-attention mechanism, proposed model effectively captures discriminative features and improves classification of dimentia sublevels. For classifier performance optimization, the Horned Lizard Optimization (HLO) algorithm is employed to optimize the model's hyperparameters. The proposed model is implemented in Python and achieved an accuracy of 98.98%,precision of 99.12%, recall of 98.96%, F-score of 99.04% and specificity of 99.58%, demonstrating its effectiveness in accurate and reliable classification.