Exploring Alzheimer's disease prediction with XAI in various neural network models

Abstract

Using a number of Neural Network Models, we attempt to explore and explain the prediction of Alzheimer's in patients in various stages of the disease, using MRI imaging data. Alzheimer's disease(AD) often described as dementia is one of the major neurological dysfunctionalities among humans and does not yet have a proven detection system; unless the nal stage symptoms of AD starts to be seen. It is observed that multimodal biological, imaging and other available neuropsychological data can ensure a high percentage of separation among (AD) patients from cognitively normal elders. However, they cannot surely predict or detect early enough that patients with early signs of mild cognitive impairment (MCI) can develop into Alzheimer's disease dementia in the future. But the research done till date shows a high probable detection rate in which they used the pattern classi er built on various longitudinal data. So in this paper we experimented with the existing Neural Network models to detect Alzheimer's disease in its early stage by classi cation techniques; and will be using a recent hybrid dataset in the process to have four separate classi cation in total. And also explored the exact region for which that speci c classi cation occurs for the patients, looking at the T1 weighted MRI scans from a hybrid dataset from Kaggle [21] using the LIME based XAI(Explainable Arti cial Intelligence) framework. For the Convolution Neural Network Models we are using Resnet50, VGG16 and Inception v3 and received 82.56%, 86.82%, 82.04% of categorical accuracy respectively.