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Alzheimer’s disease affects the majority of the elderly in today’s world. It directly affects the neurotransmitters and leads to dementia. Brain MRI images can identify Alzheimer’s disease. MRI images can spot brain irregularities related to mild cognitive damage. It can help predict Alzheimer’s disease. Even though there are numerous methods for detecting Alzheimer’s disease, using MRI scan images is still a big challenge. This study used an Adaboost classifier with a hybrid PSO algorithm to propose a novel technique for detecting Alzheimer’s disease. Adaboost acted as the best classifier among other classifiers. The curvelet transform and Principal Component Analysis (PCA) initially extract and identify the best features in MRI images. This Adaboost classifier receives optimal features as input. Finally, Adaboost classifiers with MRI images produce excellent classification accuracy. To evaluate our proposed method, we used three metrics: accuracy, specificity, and sensitivity. Based on the results, our proposed methods yield greater accuracy than the existing systems.

Machine Learning Model and Cuckoo Search in a modular system to identify Alzheimer’s disease from MRI scan images