Interpretability of deep neural networks used for the diagnosis of Alzheimer's disease

Abstract

Alzheimer's disease (AD) is a chronic brain disorder and is the most common cause of dementia. Patients suffering from AD experience memory loss, confusion, and other cognitive and behavioral complications. As the disease progresses, these symptoms become severe enough to interfere with the patient's daily life. Since AD is an irreversible disease and existing treatments can only slow down its progress, early diagnosis of AD is a key moment in fighting this disease. In this article, we propose a novel approach for diagnosing AD via deep neural networks from magnetic resonance imaging images. Additionally, we propose three new propagation rules for the layer‐wise relevance propagation (LRP) method, which is a method used for visualizing evidence in deep neural networks to obtain a better understanding of the network's behavior. We also propose various rule configurations for the LRP to achieve better interpretability of the network. Our proposed classification method achieves a 92% accuracy when classifying AD versus healthy controls, which is comparable to state‐of‐the‐art approaches and could potentially aid doctors in AD diagnosis and reduce the occurrence of human error. Our proposed visualization approaches also show improvements in evidence visualization, which helps the spread of computer‐aided diagnosis in the medical domain by eliminating the “black‐box” nature of the neural networks.

Application of Explainable Artificial Intelligence in Alzheimer's Disease Classification: A Systematic Review

“…Many of the research articles have utilized datasets that includes ADNI, OASIS, and Kaggle for training AI-based AD detection models with MRI images as input data. From Table 7, the articles [114], [151], [123], [152], [94], and [125] propose classifiers of deep neural networks for prediction and classification between HC, MCI and AD. All these articles use datasets from ADNI and choose MRI images as input.…”

“…LRP creates a heatmap that explains the significance of each voxel that contributes to a specific Fig. 17: LRP Explanation -Picture Courtesy - [125] Fig. 18: LRP Explanation -Picture Courtesy - [125] classification.…”

“…17: LRP Explanation -Picture Courtesy - [125] Fig. 18: LRP Explanation -Picture Courtesy - [125] classification. The study also computes a sum of all layerwise relevance metrics of the MRI image that helps to identify critical areas of the image.…”

“…Pohl et al [125] propose LRP with multiple rules, also known as composite LRP. On the contrary, LRP with a single rule, also known as uniform LRP, uses a single rule for interpretation.…”

“…The authors focused on using AI, HCI, and XAI in healthcare. There have been several noteworthy applications of XAI in AD classification too [113,114,115,116,117,118,119,120,121,122,123,124,125,126,127] However, an exclusive systematic review article on XAI for AD classification that points to various XAI frameworks and Blackbox models used inside these frameworks is yet to be proposed by the research community.…”