Alzheimer's disease detection and classification using transfer learning technique and ensemble on convolutional neural networks

Abstract

Alzheimer's disease is a neurological disease that affects the healthy cells of the brain and results in people having long-term memory loss, thinking problems, disorientation, behavioral inconsistencies and finally death. When the disease gets detected, the pathological load is already high, and there is no coming back from there. This neurodegenerative disease consists of three general stages, which we classified in this research and that includes very mild (early stage), mild (middle stage) and finally, the moderate stage (late-stage). Using transfer learning, we implemented five existing efficient and recent Convolutional Neural Networks (CNN) models such as VGG19, Inception- ResNetv2, ResNet152v2, EfficientNetB5 and EfficientNetB6, and another custom one of our own. Later, we ensembled thrice with multiple combinations of the models to enhance our outcome. This led us to achieve our proposed model, which is a weighted average ensemble of all the six classifiers mentioned above and this novel approach gave us an accuracy of 96%, which was quite satisfactory compared to any other existing models.