Abstract
Alzheimer's disease is a degenerative neurological condition resulting in brain cell death and brain tissue loss. Most importantly, memory-related brain cells are permanently harmed due to this condition. Alzheimer's disease diagnosis is a challenging task due to its high discriminative feature representation for classification using traditional machine learning (ML) methods. These challenges exist due to similar brain processes and pixel intensities. To overcome the above mentioned drawbacks, hybrid feature extraction techniques such as Gray Level Run Length Matrix (GLRLM), Gabor wavelet transform and Local Energy-based Shape Histogram (LESH) are used. In this designed model, Alzheimer's disease is predicted using brain MRI. At first, the collected magnetic resonance imaging (MRI) of the brain are resized and enhanced using the image resizing and BW-net technique. Features from these enhanced images are extracted using the GLRLM, Gabor wavelet transform and LESH techniques for shape, texture and edge of the brain MRI. Then, the extracted features are optimally selected using the SEAGULL optimization technique. These optimally selected features are trained using the modified DNN for predicting Alzheimer's disease. Performance metrics for proposed and existing models are studied and contrasted in order to assess the planned model. For the proposed model, 91%, 2%, 98% and 97% are performance metrics that were reached in aspects of precision, error, accuracy and recall. Thus, designed Alzheimer's disease prediction using modified DNN with optimal feature selection based on seagull optimization performs better and accurately predicts Alzheimer's disease.