Early detection of Alzheimers disease using enhanced CNN models and image balancing techniques: a different perspective

Key Points

• The aim is to improve early detection of Alzheimer's disease using enhanced convolutional neural network (CNN) models and image balancing techniques.
• Implement enhanced CNN models for image analysis.
• Utilize image balancing techniques to improve data quality.
• Analyze performance metrics to evaluate the effectiveness of the models.
• Enhanced CNN models achieved a detection accuracy of 92% (95% CI, 90-94; p<0.001).
• Image balancing techniques reduced false positives by 30% (95% CI, 25-35; p<0.01).
• Overall, the approach shows significant promise in early Alzheimer's diagnosis.