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The most prevalent cause of dementia in older people is Alzheimer's disease (AD). Dementia is a neurological disease that severely limits a person's capacity to do everyday tasks. The importance of early Alzheimer's disease diagnosis can be attributed to several factors. Most of them provide for Alzheimer's disease therapies that can slow down the disease's development. The CT scan reveals a degree of generalized cortical atrophy in Alzheimer's disease patients. As a result, CT scan picture processing is critical in the early detection of Alzheimer's disease. Here, image processing is used to detect the objects in CT pictures. Edge detection is a critical first phase in image processing since it defines the discontinuities in gray-level images. The majority of them are clinic-based structural MRI images with small sample size and few scanning layers. Deep learning, on the other hand, necessitates a large amount of annotated details. This paper suggests a dataset increment approach based on a weighted mixture of positive and negative tests and a learning method with a limited number of samples to meet the realistic requirements of clinical evaluation of Alzheimer's disease. It produces a GKFCM Clustering model that can collect more image feature details and boost the model's generalization ability.