There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Deniz Mert Tekin |
| Institution: | University of Kocaeli |
| Department: | Computer Engineering |
| Country: | |
| Proposed Analysis: | In this proposed analysis, we aim to develop a novel approach for diagnosing Alzheimer's disease in patients using signal and image processing technology. The project will involve the following key steps: Data Collection: Gather a diverse dataset of brain images and signals from both healthy individuals and patients with Alzheimer's disease. Ensure the dataset's quality and appropriate annotations for accurate diagnosis. Preprocessing: Implement image processing techniques to enhance the quality of brain images, such as denoising and image registration. Apply signal preprocessing methods to remove artifacts and noise from brain signals. Feature Extraction: Utilize advanced feature extraction methods to extract relevant features from brain images and signals. Select features that are most discriminative in differentiating between healthy and Alzheimer's-affected brains. Machine Learning Model: Develop a robust machine learning model, such as a convolutional neural network (CNN) for image analysis and a recurrent neural network (RNN) for signal analysis. Train the model on the preprocessed data and optimize its hyperparameters for optimal performance. Classification and Diagnosis: Employ the trained model to classify new brain images and signals into healthy or Alzheimer's-affected categories. Establish a reliable diagnostic criterion based on the model's predictions and probability scores. Validation and Evaluation: Validate the performance of the proposed approach using various metrics, such as accuracy, sensitivity, specificity, and area under the curve (AUC). Compare the results with existing diagnostic methods to assess the effectiveness of the proposed analysis. Ethical Considerations: Ensure the privacy and confidentiality of patient data throughout the project. Comply with all ethical guidelines and obtain necessary approvals for data usage and research. By following this proposed analysis, we aim to develop an accurate and reliable system for diagnosing Alzheimer's disease in patients, which could significantly aid in early detection and timely treatment of the condition. |
| Additional Investigators |
