Assessing the Use of Transfer Learning in Neural Networks Used to Assist in the Diagnosis of Alzheimer’s Disease

The analysis of neuroimaging data by means of computer systems has become a general practice in the diagnosis and monitoring of Alzheimer’s disease. In recent years, different systems based on neural networks have been proposed to aid in the diagnosis of this disorder. These systems usually contain millions of parameters that must be adjusted during the training process. This requires large datasets, often not available in most studies. The use of pre-trained systems (also known as transfer learning) would help to alleviate this problem, however these systems are often pre-trained with data of a very different nature than neuroimaging data, so their use could be counterproductive. In this work we evaluate the use of transfer learning for the development of a computer-aided diagnosis system for Alzheimer’s disease. To this end, we compared the performance obtained by different systems with and without transfer learning. The results show that transfer learning improves the results in some cases, reaching higher performances than other systems, however, the improvement depends largely on the optimizer used. In addition, we propose a novel method to convert neuroimaging data from 3D volumes to the 2D images used as input for most pre-trained systems.