Classification of Alzheimer's Disease using Transfer Learning and Support Vector Machine
Keywords:
Alzheimer's Disease, Dementia, Brain Deterioration, Early Diagnosis, Magnetic Resonance Imaging (MRI), Automated Detection, Classification System, Transfer Learning, Convolutional Network, Alexnet, KAGGLE DatasetAbstract
Alzheimer's disease, characterized by cognitive decline due to impaired brain cells, remains without a definitive cure. However, early diagnosis can substantially mitigate its impact and improve patient management. Recognizing this, we developed an automated system for interpreting brain Magnetic Resonance Imaging (MRI) scans, aiming not just to detect dementia but also to classify its various stages. Using a transfer learning method, we adapted the AlexNet convolutional network to this specific challenge, training it primarily on un-segmented MRI images. Our model, when tested on the publicly accessible KAGGLE dataset, demonstrated a significant accuracy of 94.49% for multi-class classification. Additionally, our model's prowess wasn't limited to accuracy alone. In multi-class scenarios, it reported a specificity of 97.78%, precision of 77.42%, recall of 94.49%, and an F1-score of 81.08%. Impressively, it surpassed contemporaneous studies, outdoing the 79.8% accuracy of Tooba et al. and the 62.7% by S0rensen et al. The ROC curve further highlighted the model's proficiency in distinguishing between dementia stages, with 'Moderate Dementia' reaching an AUC of 0.97964. Such results underline not only the efficacy of our approach but also its promise as a groundbreaking asset in Alzheimer's diagnostics.
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