Review of “Alzheimer’s Prediction using Deep Learning”: A Comprehensive Study

Review of “Alzheimer’s Prediction using Deep Learning”: A Comprehensive Study

Mrs. Geetanjali N. Sawant
Assisant Professor, Finolex Academy of Management & Technology, Ratnagiri..
University of Mumbai
@famt.ac.in

Miss. Purva S. Chavan

Student, Finolex Academy of Management and Technology, Ratnagiri. University of Mumbai

purv334@gmail.com

Miss. Sanika S. Salvi

Student, Finolex Academy of Management and Technology, Ratnagiri. University of Mumbai

salvisanika66@gmail.com

Abstract— Alzheimer’s disease is an incurable neurological disorder that leads to a gradual decline in cognitive abilities, but early detection can significantly mitigate symptoms. The automatic diagnosis of Alzheimer’s disease is more important due to the shortage of expert medical staff, because it reduces the burden on medical staff and enhances the results of diagnosis. A detailed analysis of specific brain disorder tissues is required to accurately diagnose the disease via segmented magnetic resonance imaging (MRI). Several studies have used the traditional machine-learning approaches to diagnose the disease from MRI, but manual extracted features are more complex, time-consuming, and require a huge amount of involvement from expert medical staff. The traditional approach does not provide an accurate diagnosis. Deep learning has automatic extraction features and optimizes the training process. The Magnetic Resonance Imaging (MRI) Alzheimer’s disease dataset consists of four classes: mild demented (896 images), moderate demented (64 images), non-demented (3200 images), and very mild demented (2240 images). The dataset is highly imbalanced. Therefore, we used the adaptive synthetic oversampling technique to address this issue. After applying this technique, the dataset was balanced. The ensemble of EfficientNetB3 was used to detect Alzheimer’s disease on both imbalanced and balanced datasets to validate the performance of the models. The proposed method combined the predictions of multiple models to make an ensemble model that learned complex and nuanced patterns from the data. The input and output of both models were concatenated to make an ensemble model and then added to other layers to make a more robust model. In this study, we proposed an ensemble of EfficientNet B3 to diagnose the disease at an early stage with the highest accuracy. Experiments were performed on two publicly available datasets. The experimental results showed that the proposed method achieved 99.5% accuracy. We evaluated that the proposed method was extremely efficient and provided superior performance on both datasets as compared to previous methods.

Keywords—Deep learning, Neural network, Convolutional neural network, Recurrent neural network, Feature extraction, PET scan, MRI analysis.