Detection of Alzheimer's Disease Stages Using Pre-Trained Deep Learning Approaches

Alzheimer's disease (AD) is a form of dementia that is irreversible in nature with no effective cure till date. It ranks seventh in terms of causing mortality, predominantly impacting the elderly demographic. Therefore, its early diagnosis is an important concern for controlling the progression of this disease. This paper aims to design a framework for classifying various stages of this disease using brain MRI. Nowadays, deep learning approaches has gained much attention due to its promising results in object detection and classification tasks. But the requirement of huge dataset is the most common issue with these architectures. To overcome this issue concept of Transfer Learning (TL) is adopted by many researchers to take the advantage of pre-trained models. In this work TL has been applied by fine tuning the EfficientNetB0 model on Kaggle dataset that classifies the four stages of Alzheimer's disease. The obtained result proves that the model outperforms the state-of -the-techniques achieving an accuracy of 95.78% for multi-class classification.