To develop an innovative deep learning-based approach for improving MRI image analysis in Alzheimer’s Disease.
Key Findings:
LinkNet3D achieved a Dice coefficient of 0.9715 and an IoU of 0.9446, indicating high accuracy in skull separation.
The model has 2,126,258 parameters, a reduction of ~20.4% compared to traditional U-Net, enhancing computational efficiency.
Achieved high accuracy and precision on multiple datasets, with results including 98.80% accuracy and 99.88% F1-Score on the ADNI-General dataset.
Interpretation:
The proposed framework significantly enhances MRI evaluation and lesion marking in Alzheimer’s Disease, providing clinicians with robust tools for diagnosis and analysis.
Limitations:
The study may require further validation on larger and more diverse datasets.
Potential challenges in model interpretability and complexity remain.
Conclusion:
The innovative CVTC framework demonstrates promising results in improving the accuracy and efficiency of MRI evaluations in Alzheimer’s Disease, paving the way for better diagnostic tools.
