Kanghan Oh1, Woosung Kim2, Guangfan Shen2, Yanhong Piao2, Nam-In Kang3, Il-Seok Oh1, Young Chul Chung4. 1. Division of Computer Science and Engineering, Chonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju-si 54896, Republic of Korea. 2. Research Institute of Clinical Medicine of Chonbuk National University-Biomedical Research Institute of Chonbuk National University Hospital, Jeonju-si 54907, Republic of Korea; Department of Psychiatry, Chonbuk National University Medical School, Jeonju-si 54907, Republic of Korea. 3. Department of Psychiatry, Maeumsarang Hospital, Wanju, Jeollabuk-do, Republic of Korea. 4. Research Institute of Clinical Medicine of Chonbuk National University-Biomedical Research Institute of Chonbuk National University Hospital, Jeonju-si 54907, Republic of Korea; Department of Psychiatry, Chonbuk National University Medical School, Jeonju-si 54907, Republic of Korea. Electronic address: chungyc@jbnu.ac.kr.
Abstract
BACKGROUND: The recent deep learning-based studies on the classification of schizophrenia (SCZ) using MRI data rely on manual extraction of feature vector, which destroys the 3D structure of MRI data. In order to both identify SCZ and find relevant biomarkers, preserving the 3D structure in classification pipeline is critical. OBJECTIVES: The present study investigated whether the proposed 3D convolutional neural network (CNN) model produces higher accuracy compared to the support vector machine (SVM) and other 3D-CNN models in distinguishing individuals with SCZ spectrum disorders (SSDs) from healthy controls. We sought to construct saliency map using class saliency visualization (CSV) method. METHODS: Task-based fMRI data were obtained from 103 patients with SSDs and 41 normal controls. To preserve spatial locality, we used 3D activation map as input for the 3D convolutional autoencoder (3D-CAE)-based CNN model. Data on 62 patients with SSDs were used for unsupervised pretraining with 3D-CAE. Data on the remaining 41 patients and 41 normal controls were processed for training and testing with CNN. The performance of our model was analyzed and compared with SVM and other 3D-CNN models. The learned CNN model was visualized using CSV method. RESULTS: Using task-based fMRI data, our model achieved 84.15%∼84.43% classification accuracies, outperforming SVM and other 3D-CNN models. The inferior and middle temporal lobes were identified as key regions for classification. CONCLUSIONS: Our findings suggest that the proposed 3D-CAE-based CNN can classify patients with SSDs and controls with higher accuracy compared to other models. Visualization of salient regions provides important clinical information.
BACKGROUND: The recent deep learning-based studies on the classification of schizophrenia (SCZ) using MRI data rely on manual extraction of feature vector, which destroys the 3D structure of MRI data. In order to both identify SCZ and find relevant biomarkers, preserving the 3D structure in classification pipeline is critical. OBJECTIVES: The present study investigated whether the proposed 3D convolutional neural network (CNN) model produces higher accuracy compared to the support vector machine (SVM) and other 3D-CNN models in distinguishing individuals with SCZ spectrum disorders (SSDs) from healthy controls. We sought to construct saliency map using class saliency visualization (CSV) method. METHODS: Task-based fMRI data were obtained from 103 patients with SSDs and 41 normal controls. To preserve spatial locality, we used 3D activation map as input for the 3D convolutional autoencoder (3D-CAE)-based CNN model. Data on 62 patients with SSDs were used for unsupervised pretraining with 3D-CAE. Data on the remaining 41 patients and 41 normal controls were processed for training and testing with CNN. The performance of our model was analyzed and compared with SVM and other 3D-CNN models. The learned CNN model was visualized using CSV method. RESULTS: Using task-based fMRI data, our model achieved 84.15%∼84.43% classification accuracies, outperforming SVM and other 3D-CNN models. The inferior and middle temporal lobes were identified as key regions for classification. CONCLUSIONS: Our findings suggest that the proposed 3D-CAE-based CNN can classify patients with SSDs and controls with higher accuracy compared to other models. Visualization of salient regions provides important clinical information.