2023/10/13 by Mahin Khan Mahadi, Abdullah Abdullah, Mahadi, Mahin Khan +5
Computer Science · Health Professions · Neuroscience · #Artificial Intelligence (cs.AI) #Artificial Intelligence in Healthcare #Brain Tumor Detection and Classification #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning in Healthcare
paper · pdf · doi:10.48550/arxiv.2310.08888
openalex publication_date 2023/10/13 · openalex created_date 2023/10/17 · openalex updated_date 2026/07/28
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.