Understanding Convolutional Neural Network's behavior for Alzheimer's disease on MRI

Alzheimer's disease (AD) is the most prevalent type of dementia, resulting in gradual memory and cognitive impairment. Radiomic characteristics acquired from brain MRI have shown significant promise as non-invasive indicators for this illness. However, their use for particular brain areas has not yet been investigated. To study Alzheimer's disease (AD), conventional machine learning approaches have evolved from image decomposition methods like principal component analysis to more complicated, non-linear algorithms. Now that the deep learning paradigm has arrived, it's feasible to extract high-level abstract features directly from MRI scans that characterize how data is distributed in low-dimensional manifolds internally. In this paper, we proposed a new Convolutional Neural Network based architecture for classifying Alzheimer's disease. The model is also evaluated using performance measures like precision, recall, f1 score, confusion matrix, etc. The proposed model was evaluated and achieved a classification accuracy of 97% for the classification of 4 different classes, which surpassed almost all cutting-edge technologies.