A Hybrid Transfer Learning Assisted Decision Support System for Accurate
  Prediction of Alzheimer Disease
Mahin Khan Mahadi Abdullah Abdullah Jamal Uddin Asif Newaz
Alzheimer's disease (AD) is the most common long-term illness in elderly
people. In recent years, deep learning has become popular in the area of
medical imaging and has had a lot of success there. It has become the most
effective way to look at medical images. When it comes to detecting AD, the
deep neural model is more accurate and effective than general machine learning.
Our research contributes to the development of a more comprehensive
understanding and detection of the disease by identifying four distinct classes
that are predictive of AD with a high weighted accuracy of 98.91%. A unique
strategy has been proposed to improve the accuracy of the imbalance dataset
classification problem via the combination of ensemble averaging models and
five different transfer learning models in this study.
EfficientNetB0+Resnet152(effnet+res152) and
InceptionV3+EfficientNetB0+Resnet50(incep+effnet+res50) models have been
fine-tuned and have reached the highest weighted accuracy for multi-class AD
stage classifications.