Identification of Alzheimer's disease and mild cognitive impairment using multimodal sparse hierarchical extreme learning machine

Different modalities such as structural MRI, FDG‐PET, and CSF have complementary information, which is likely to be very useful for diagnosis of AD and MCI. Therefore, it is possible to develop a more effective and accurate AD/MCI automatic diagnosis method by integrating complementary information of different modalities. In this paper, we propose multi‐modal sparse hierarchical extreme leaning machine (MSH‐ELM). We used volume and mean intensity extracted from 93 regions of interest (ROIs) as features of MRI and FDG‐PET, respectively, and used p‐tau, t‐tau, and A β 42as CSF features. In detail, high‐level representation was individually extracted from each of MRI, FDG‐PET, and CSF using a stacked sparse extreme learning machine auto‐encoder (sELM‐AE). Then, another stacked sELM‐AE was devised to acquire a joint hierarchical feature representation by fusing the high‐level representations obtained from each modality. Finally, we classified joint hierarchical feature representation using a kernel‐based extreme learning machine (KELM). The results of MSH‐ELM were compared with those of conventional ELM, single kernel support vector machine (SK‐SVM), multiple kernel support vector machine (MK‐SVM) and stacked auto‐encoder (SAE). Performance was evaluated through 10‐fold cross‐validation. In the classification of AD vs . HC and MCI vs . HC problem, the proposed MSH‐ELM method showed mean balanced accuracies of 96.10% and 86.46%, respectively, which is much better than those of competing methods. In summary, the proposed algorithm exhibits consistently better performance than SK‐SVM, ELM, MK‐SVM and SAE in the two binary classification problems (AD vs . HC and MCI vs . HC).