Convolutional neural network based data interpretable framework for Alzheimer’s treatment planning

Abstract

Alzheimer’s disease (AD) is a neurological disorder that predominantly affects the brain. In the coming years, it is expected to spread rapidly, with limited progress in diagnostic techniques. Various machine learning (ML) and artificial intelligence (AI) algorithms have been employed to detect AD using single-modality data. However, recent developments in ML have enabled the application of these methods to multiple data sources and input modalities for AD prediction. In this study, we developed a framework that utilizes multimodal data (tabular data, magnetic resonance imaging (MRI) images, and genetic information) to classify AD. As part of the pre-processing phase, we generated a knowledge graph from the tabular data and MRI images. We employed graph neural networks for knowledge graph creation, and region-based convolutional neural network approach for image-to-knowledge graph generation. Additionally, we integrated various explainable AI (XAI) techniques to interpret and elucidate the prediction outcomes derived from multimodal data. Layer-wise relevance propagation was used to explain the layer-wise outcomes in the MRI images. We also incorporated submodular pick local interpretable model-agnostic explanations to interpret the decision-making process based on the tabular data provided. Genetic expression values play a crucial role in AD analysis. We used a graphical gene tree to identify genes associated with the disease. Moreover, a dashboard was designed to display XAI outcomes, enabling experts and medical professionals to easily comprehend the prediction results.

Exploring Types of Photonic Neural Networks for Imaging and Computing—A Review

“…Recurrent neural networks (RNNs) host the crucial concept of feedback loops, enabling them to process sequential data with temporal dependencies, making them essential for tasks, for instance, natural language processing and time series analysis [35]. Convolutional neural networks (CNNs) excel in image and video processing tasks, leveraging shared weights and local connectivity to derive hierarchical features, making them indispensable in computer vision applications [36,37]. Reservoir computing (RC), a subset of recurrent networks, offers advantages in processing temporal data efficiently, particularly in tasks In this section, we delved into several pivotal types of PNNs that stand as focal points in imaging and computing research, illuminating their significance and widespread exploration in the field, as presented in Figure 2.…”