| According to the latest estimates, one in three elderly people will suffer from dementia in 2050, with the majority of cases being Alzheimer's disease. This study proposes a novel deep-learning CNN architecture for the prediction of Alzheimer's disease, utilizing the OASIS-2 dataset. On average, the proposed approach achieves a testing accuracy of 96.37% for 100-fold cross validation, outperforming related works such as VGG-19 [4], AlexNet [3], and GoogLeNet [3] by 0.55%, 4.97%, and 3.35%, respectively. Experimental results provide positive evidence that the proposed approach, with much fewer parameters, has strong potential to tackle the prediction problem of Alzheimer's disease. In the future, a specialized system can be developed to handle Alzheimer's disease and alleviate the diagnostic burden of doctors. This intelligent system can collaborate with doctors in the early stages of diagnosis, reducing the burden of consultations. If the system achieves higher accuracy, it can enable early detection and treatment, delaying the progression of symptoms. Such a system can detect Alzheimer's disease early, monitor the patient's condition over time, provide personalized treatment plans, and achieve better results. |
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