Diagnosis of brain disease based on the deep learning algorithms of neural imaging techniques

Article type: Research Article

Authors: Wang, Qian^{; *}

Affiliations: Beijing TianTan Hospital, Capital Medical University, Beijing, China

Correspondence: [*] Corresponding author. Qian Wang, Beijing TianTan Hospital, Capital Medical University, Beijing 00029, China. E-mail: [email protected].

Abstract: Neuroimaging technology is considered a non-invasive method research the structure and function of the brain which have been widely used in neuroscience, psychiatry, psychology, and other fields. The development of Deep Learning Neural Network (DLNN), based on the deep learning algorithms of neural imaging techniques in brain disease diagnosis plays a more and more important role. In this paper, a deep neural network imaging technology based on Stack Auto-Encoder (SAE) feature extraction is constructed, and then Support Vector Machine (SVM) was used to solve binary classification problems (Alzheimer’s disease [AD] and Mild Cognitive Impairment [MCI]). Four sets of experimental data were employed to perform the training and testing stages of DLNN. The number of neurons in each of the DLNNs was determined using the grid search technique. Overall, the results of DLNNs performance indicated that the SAE feature extraction was superior over (Accuracy Rate [AR] = 74.9% with structure of 93-171-49-22-93) shallow layer features extraction (AR = 70.8% with structure of 93-22-93) and primary features extraction (AR = 69.2%).

Keywords: Deep learning neural network, neuroimaging technology, brain diseases, disease diagnosis, feature extraction

DOI: 10.3233/JIFS-237979

Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10201-10212, 2024