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Alzheimer Diagnosis Through Advanced Deep Learning Architectures and Interpretative Analysis of Predictions

Abstract:

Dementia, a critical global health challenge recognized by the World Health Organization (WHO), affects millions of lives, with more than 50 million cases reported in 2019, a figure projected to double by 2050. Among its forms, Alzheimer’s disease is the most prevalent, underscoring the urgent need for early detection to improve patient outcomes and mitigate societal impact. Leveraging recent advancements in artificial intelligence, this study introduces an innovative deep learning framework aimed at revolutionizing the diagnostic process, providing valuable insights for the scientific community and practical tools for medical professionals. The proposed approach is structured into five key phases: data collection, preprocessing, model training using transfer learning, quality metrics validation including Accuracy, Precision, Recall, and F1 Score—and result interpretation through integrated gradients. A robust dataset of over 40,000 MRI images was utilized, achieving an exceptional accuracy of 99.86% in classifying the stages of Alzheimer’s disease. To ensure interpretability, integrated gradients were employed to highlight critical neuroanatomical markers, such as cortical atrophy and enlarged ventricles, distinguishing patients with dementia from healthy individuals. These findings validate the model’s reliability and demonstrate its potential as an innovative tool for advancing Alzheimer’s diagnosis and care.