Improving Alzheimer's stage categorization with Convolutional Neural Network using transfer learning and different magnetic resonance imaging modalities

Abstract

Background Alzheimer's Disease (AD) is a neurodegenerative disease characterized by progressive loss of memory and general decline in cognitive functions. Multi-modal imaging such as structural MRI and DTI provide useful information for the classification of patients on the basis of brain biomarkers. Recently, CNN methods have emerged as powerful tools to improve classification using images. New Method In this paper, we propose a transfer learning scheme using Convolutional Neural Networks (CNNs) to automatically classify brain scans focusing only on a small ROI: e.g. a few slices of the hippocampal region. The network's architecture is similar to a LeNet-like CNN upon which models are built and fused for AD stage classification diagnosis. We evaluated various types of transfer learning through the following mechanisms: (i) cross-modal (sMRI and DTI) and (ii) cross-domain transfer learning (using MNIST) (iii) a hybrid transfer learning of both types. Results Our method shows good performances even on small datasets and with a limited number of slices of small brain region. It increases accuracy with more than 5 points for the most difficult classification tasks, i.e., AD/MCI and MCI/NC. Comparison with Existing Method(s) Our methodology provides good accuracy scores for classification over a shallow convolutional network. Besides, we focused only on a small region; i.e., the hippocampal region, where few slices are selected to feed the network. Also, we used cross-modal transfer learning. Conclusions Our proposed method is suitable for working with a shallow CNN network for low-resolution MRI and DTI scans. It yields to significant results even if the model is trained on small datasets, which is often the case in medical image analysis.

Multi-modality MRI fusion for Alzheimer’s disease detection using deep learning

“…They proposed different combination techniques to ensure the best classification. Aderghal et al (29) suggested the transfer learning technique to perform the fusion process and Marzban et al (30)adopted a cascaded CNN. However, they achieved lower accuracy than what we got which is over 97%.…”

“…After that, the pre-trained VGG16 network is used to extract the features and SVM classifier to discriminate MCI patients, from CN using the ADNI dataset. Aderghal et al (29) proposed LeNet-like CNN based on sMRI and DTI-MD images. They selected the median slice Hippocampal and its two neighbors in each projection (axial, sagittal, and coronal).…”

Review of white box methods for explanations of convolutional neural networks in image classification tasks

“…They can produce promising results that outperform human capabilities in various decision tasks relating to visual content classification and understanding such as face detection, 1 object detection and segmentation, 2 image denoising, 3 video-based tasks such as sports action recognition 4 and saliency detection, 5 and among others. The success of deep learning-based systems in these tasks have also paved the way for their applications to be developed for a variety of medical diagnosis tasks such as cancer detection 6 and Alzhiemer's disease detection 7 on different imaging modalities just to name a few. Along with the usefulness of these tools, the trustfulness and reliability of such systems are also being questioned.…”

White Box Methods for Explanations of Convolutional Neural Networks in Image Classification Tasks

“…They can produce promising results that outperform human capabilities in various decision tasks relating to visual content classification and understanding such as face detection, 1 object detection and segmentation, 2 image denoising, 3 video-based tasks like sports action recognition 4 and saliency detection 5 amongst others. The success of deep learning-based systems in these tasks have also paved the way for their applications to be developed for a variety of medical diagnosis tasks like cancer detection, 6 and Alzhiemer's disease detection 7 on different imaging modalities just to name a few. Along with the usefulness of these tools, the trustfulness and reliability of such systems is also being questioned.…”