Alzheimer’s disease, which currently affects more than 40 million people, is the most common neurodegenerative disease in elder people. Early diagnosis is crucial both to treat the disease and to help the development of new medicines, as it hasn’t been possible to find a cure so far. The development of Alzheimer’s has been proven to be closely linked to structural changes -related to the gray matter, responsible for processing information- and functional ones -related to the white matter, which connects the different regions of the brain through fibers- in the brain connectivity network, since a significant loss of fibers also causes functional alternations, such as memory loss.
However, diagnosis remains a challenge in spite of the scientific advances made, and to date it hasn’t been possible to determine how functional cerebral activity deteriorates the structural one and vice versa, which is a key element to better understand the development of this type of diseases.
In this regard, computer aided diagnosis (CAD) is an important tool since it helps physicians to understand multimedia 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, 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.
Researchers Andrés Ortiz, Jorge Munilla, Juan Górriz and Javier Ramírez (from the universities of Málaga and Granada) have recently published, in the renowned International Journal Of Neural Systems, a similar article called Ensembles of deep learning architectures for the early diagnosis of the Alzheimer’s disease. Said study presents a method for the diagnosis of Alzheimer’s by the fusion of functional and structural images based on the use of the deep learning technique.
