Can I read A Hybrid Transfer Learning Assisted Decision Support System for Accurate Prediction of Alzheimer Disease on EtoBox?

A Hybrid Transfer Learning Assisted Decision Support System for Accurate Prediction of Alzheimer Disease by Mahadi, Mahin Khan; Abdullah, Abdullah; Uddin, Jamal; Newaz, Asif is a scholarly article available to read on EtoBox.

What is A Hybrid Transfer Learning Assisted Decision Support System for Accurate Prediction of Alzheimer Disease about?

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.

Author

Mahadi, Mahin Khan; Abdullah, Abdullah; Uddin, Jamal; Newaz, Asif

Published

2023

Language

EN