A Comparative Study Using Deep Learning Models And Transfer Learning for Detection And Classification of Alzheimer’s Disease

Abstract

Recently, the burgeoning disciplines of Machine Learning (ML) and Deep Learning (DL) have experienced considerable integration across diverse scientific domains. Of significant note is their integration into the medical sector, specifically in the intricate methodologies of pathological categorization. Present-day innovations underscore the pivotal role of Deep Convolutional Neural Networks (DCNN) in mediating the tasks of image-based taxonomies and prognostications within this domain. In this research, a new DCNN with different modified intelligent architectures like CNN, modified VGG-16, VGG-19, ResNet50, and DenseNet121, besides the newly added classification layer, was implemented and tested for the detection and classification of Alzheimer’s disease. The evaluation and performance metrics are accuracy, loss, f1-score, precision, and recall. Experiments were made on Kaggle-based dataset and test results show that the CNN-based model is the most accurate model, with the highest accuracy of 96% and the lowest loss of 9.92%. Finally, the average performance percentage of the overall proposed model is as follows: accuracy is 91%, loss is 19.75%, precision is 89.4%, F1-score is 88.83%, and recall is 90%. Index Terms— Deep Learning, Transfer Learning, Alzheimer’s Disease, DCNN, CNN.