Version 1
: Received: 27 August 2024 / Approved: 28 August 2024 / Online: 28 August 2024 (10:41:59 CEST)
How to cite:
Fareed, M. M. S.; Ahmed, G.; Zikria, S.; Jamal, S. A. Bio-AD: A Transfer Learning Approach for Diagnosis of Alzheimer’s Diseases in MRI Scans. Preprints2024, 2024082010. https://doi.org/10.20944/preprints202408.2010.v1
Fareed, M. M. S.; Ahmed, G.; Zikria, S.; Jamal, S. A. Bio-AD: A Transfer Learning Approach for Diagnosis of Alzheimer’s Diseases in MRI Scans. Preprints 2024, 2024082010. https://doi.org/10.20944/preprints202408.2010.v1
Fareed, M. M. S.; Ahmed, G.; Zikria, S.; Jamal, S. A. Bio-AD: A Transfer Learning Approach for Diagnosis of Alzheimer’s Diseases in MRI Scans. Preprints2024, 2024082010. https://doi.org/10.20944/preprints202408.2010.v1
APA Style
Fareed, M. M. S., Ahmed, G., Zikria, S., & Jamal, S. A. (2024). Bio-AD: A Transfer Learning Approach for Diagnosis of Alzheimer’s Diseases in MRI Scans. Preprints. https://doi.org/10.20944/preprints202408.2010.v1
Chicago/Turabian Style
Fareed, M. M. S., Shahid Zikria and Saad Ahmed Jamal. 2024 "Bio-AD: A Transfer Learning Approach for Diagnosis of Alzheimer’s Diseases in MRI Scans" Preprints. https://doi.org/10.20944/preprints202408.2010.v1
Abstract
Alzheimer’s is a degenerative brain condition brought on by intricate brain changes after cell injury. The prevalence of Alzheimer’s disease is highest in those over 65. Presently, AD is detected by manual examination of a patient’s Magnetic Resonance Imaging (MRI) scan and neuropsychological assessments. Due to its subpar accuracy and performance, solutions based on artificial intelligence (AI) have not been made more widely available. This paper proposes a deep Convolutional Neural Network (CNN) with the pre-trained MobileNet-V2 architecture as the model’s base for feature extraction and reducing the parameter size and training time. The proposed model accurately classifies various early stages of AD. The proposed approach visualizes the class activation maps in the form of a heat map on brain MRI scans via the Grad-CAM algorithm that lifts the black-box nature of the deep model. To handle the imbalance problem of the AD data set acquired from Kaggle, the oversampling algorithm SMOTE interpolates new images to even the class samples. The proposed multi-class classification deep model is named Bio-AD and evaluated using different quality metrics. The Bio-AD model has the following values for evaluation metrics: 97.92%, 99.87%, 97.87%, 98%, 98%, and 0.0646 for accuracy, Area Under the Curve (AUC), F1-score, Precision, Recall, and loss respectively. The proposed Bio-AD model is compared with DenseNet-169, VGG-19, and InceptionResNet-V2 using different quality metrics and outperformed in comparison with all other models using the AD data set.
Keywords
Deep Learning; Image Classification; Supervised Learning; Transfer Learning; Imbalanced data set; MRI data set; Computer-Aided Diagnosis; SMOTE; Class Activation
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
