Efficient Image Classification for Alzheimer’s Disease Prediction Using Capsule Network

Abstract

Alzheimer’s disease is a kind of brain disorder that affects older adults. People with this disease tend to lose their memory capacity completely, eventually ending up with an inability to carry out even the simple day to day tasks. This is a permanent brain disorder that leads to Demnatia. Though there is no cure for this disease, the early prediction of this disease, at its onset could help reduce the adverse effects of the disease. Machine learning algorithms have been excelling in many fields especially in medical diagnosis for early predictions of various diseases. Psychological parameters like age, sex, urine protein level, family illness etc., have been used so far for the prediction of the disease. This paper focuses on the prediction of the disease using the MRI images of the brain. The detection of plaques and tangles in the brain is considered to be the major feature for the prediction of this disease. Convolutional Neural Network has made successful predictions in images. Hence, a variant of CNN, which is Faster R-CNN using Capsule Networkarchitecture is used in this paper for detecting and localizing the plaques and tangles present in the brain. The model is compared with the other prediction techniques and it has yielded an average prediction of 93.5% with kaggle dataset.