Alzheimer's disease is the most common neurodegenerative disease in the elderly, which currently affects more than 40 million people.
‘Computer Aided Diagnosis forms an important tool for the early diagnosis of Alzheimer’s Disease thus allowing the application of treatments that can be simpler and effective.’
There is no cure for this condition and early diagnosis is crucial for the treatment of the disease and to help in the development of new medicines.
Alzherimer's disease is closely linked to structural and functional changes in the brain.
The structural changes related to Alzheimer's disease occurs in the gray matter, which is responsible for processing information and the functional changes occurs in the white matter, which connects the different regions of the brain through fibers.
But in spite of the scientific advancement made, diagnosis and treatment still remains a challenge.
Using CAD to Understand Functional and Structural Changes in Alzheimer's Disease
Computer aided diagnosis (CAD) helps physicians to understand the content obtained in tests carried out in patients, which allows a simpler and more effective application of the treatment.
One such procedure is medical imaging, which provides high resolution "live" information on the subject matter and allows the use of information related to the disease contained in the image.
The BioSip research team Andrés Ortiz, Jorge Munilla, Juan Górriz and Javier Ramírez, belonging to the University of Malaga, in collaboration with a group of researchers from the University of Granada, has been studying biomedical images and signals for years.
The study helps in the diagnosis of Alzheimer's by the fusion of functional and structural images based on the use of the deep learning technique.
By automatically extracting the affected regions of interest, the artificial Intelligence (AI) technique will enable computers to differentiate the brain of a healthy person from that of an ill person.
The researchers explain, "the study uses deep learning techniques to calculate brain function predictors and magnetic resonance imaging to prevent Alzheimer's disease. To do this, we have used different neural networks with which to model each region of the brain to combine them afterwards".
Classification Based on Deep Learning Architectures
Deep Learning architectures is applied to brain regions as defined by the Automated Anatomical Labeling (AAL), a digital atlas of the human brain.
Based on the deep learning architectures, the study explores the construction of classification methods.
The images of gray matter from each brain area have been divided according to the regions defined by the AAL atlas and these patches are used to train different deep belief networks.
This helps powerful classification architecture to automatically extract the most relevant features of a set of images. The proposed method has been evaluated using a large database from the Alzheimer's Disease Neuroimaging Initiative (ADNI).
The results of this work show the potential of AI techniques to reveal patterns associated with the disease.
This method can be used to understand the neurodegenerative process involved in the development of the disease, besides being useful as a starting point for the development of more effective medical treatments.
The techniques developed may also help in improvement of accuracy in the diagnosis of other forms of dementia such as Parkinson's disease.
The study 'Ensembles of Deep Learning Architectures for the Early Diagnosis of the Alzheimer's Disease' is published in International Journal Of Neural Systems
- Andrés Ortiz et al. Ensembles of Deep Learning Architectures for the Early Diagnosis of the Alzheimer's Disease. International Journal Of Neural Systems; (2017) doi.org/10.1142/S0129065716500258