Deep learning enthused hybrid framework for early diagnosis of mild cognitive disorder caused by Alzheimer’s disease

Memory cells in the brain are permanently damaged in Alzheimer's disease, making it a common cause of dementia. It is possible that early diagnosis of this condition may aid medical professionals in the treatment of patients and the prevention of disease progression. Anatomical changes in the brain's diseased context may be assessed by MRI (magnetic resonance imaging), regardless of other neuroimaging-based diagnostic procedures. MRI diagnosis has shown generally sufficient results in terms of classifying the MRI picture from normal to Alzheimer's disease, according to existing research papers on the subject. However, in many clinical circumstances, MRI results are indifferent between the normal controls and the MCD (Moderate Cognitive Decline/Impairment) phase of the study time frame. For this challenge, a deep learning inspired optimization model is presented in the study that helps in the correct classification of MRIs of normal and MCD. Experiments have shown that the suggested model outperforms a number of current methods for picture segmentation

Cognition · Cognitive decline · Context (archaeology · Dementia · Disease · Magnetic resonance imaging · Neuroimaging · Pathology · Radiology · AI in cancer detection · Brain Tumor Detection and Classification · Machine Learning in Healthcare · Medicine · Neuroscience · Psychology