Enhanced Fuzzy Elephant Herding Optimization-Based OTSU Segmentation and Deep Learning for Alzheimer’s Disease Diagnosis

Several neurological illnesses and diseased sites have been studied, along with the anatomical framework of brain, using structural MRI (sMRI). It is critical to diagnose Alzheimer’s disease (AD) patients in a timely manner implement preventative treatments. The segmentation brain anatomy categorization AD received increased attention since they can deliver good findings spanning vast range information. first research gap considered this work real-time efficiency OTSU segmentation, which not high, despite its simplicity accuracy. A second issue that feature extraction could be automated by implementing deep learning techniques. To improve picture segmentation’s real-timeliness, enhanced fuzzy elephant herding optimization (EFEHO) was used for named EFEHO-OTSU. main contribution twofold. One utilizing EFEHO recommended technique seek optimal threshold method. Second, dual multi-instance network (DA-MIDL) diagnosis prodromal phase, mild cognitive impairment (MCI). Tests show converges faster takes less time than classic approach without reducing performance. This study develops valuable tool quick efficiency. Compared numerous conventional techniques, suggested attains improved performance regarding accuracy transferability.