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: | Dewinda Julianensi Rumala |
| Institution: | Sepuluh Nopember Institute of Technology |
| Department: | S3. Electrical Eng. |
| Country: | |
| Proposed Analysis: | The brain is a complex organ that is very important for humans to control many things. However, the brain disease that attacks humans can interfere with an individual's daily life. In the worst case, this disease can be fatal to the point that it might cause death. Therefore, brain disease diagnosis is urgently needed so that doctors can provide further action to patients suffering from it, be it prevention, treatment, or further treatment. Diagnosis of brain disease usually takes a long time because it used to be carried out invasively through biopsy. However, with the advent of imaging techniques, the current diagnosis of brain disease can be performed non-invasively. MRI is one of the safest brain imaging techniques, and many data have been obtained using this imaging technique. This abundance of data offers opportunities for researchers in the health sector to diagnose diseases more quickly and accurately, especially with the existence of Deep Learning algorithms. Convolutional Neural Network (CNN) is a Deep Learning method that has been proven to have advantages over conventional Machine Learning algorithms. CNN has the advantage of performing automatic extraction, so experts often use it for image classification and segmentation, including medical images. This study will develop a method for identifying brain diseases and their severity based on Magnetic Resonance Images (MRI) using Deep Learning. The developed Deep Learning model will perform analysis based on multi-slice and volumetric. The model is designed and arranged in specific ways so that it can perform the tasks automatically. It is hoped that the results obtained in this study can be used by experts to determine the type of brain disease and its severity quickly and accurately so that further action can be given to patients suffering from the disease. |
| Additional Investigators |
