Explaining decisions of a light-weight deep neural network for real-time coronary artery disease classification in magnetic resonance imaging

Abstract

In certain healthcare settings, such as emergency or critical care units, where quick and accurate real-time analysis and decision-making are required, the healthcare system can leverage the power of artificial intelligence (AI) models to support decision-making and prevent complications. This paper investigates the optimization of healthcare AI models based on time complexity, hyper-parameter tuning, and XAI for a classification task. The paper highlights the significance of a lightweight convolutional neural network (CNN) for analysing and classifying Magnetic Resonance Imaging (MRI) in real-time and is compared with CNN-RandomForest (CNN-RF). The role of hyper-parameter is also examined in finding optimal configurations that enhance the model's performance while efficiently utilizing the limited computational resources. Finally, the benefits of incorporating the XAI technique (e.g. GradCAM and Layer-wise Relevance Propagation) in providing transparency and interpretable explanations of AI model predictions, fostering trust, and error/bias detection are explored. Our inference time on a MacBook laptop for 323 test images of size 100x100 is only 2.6 sec, which is merely 8 milliseconds per image while providing comparable classification accuracy with the ensemble model of CNN-RF classifiers. Using the proposed model, clinicians/cardiologists can achieve accurate and reliable results while ensuring patients' safety and answering questions imposed by the General Data Protection Regulation (GDPR). The proposed investigative study will advance the understanding and acceptance of AI systems in connected healthcare settings.

Keywords: Classification; Explainable AI; Healthcare models; Hyper-parameter tuning; Time complexity.

Fig. 1

Example of 2D MRI/CMR images from CAD patients (a–c) and healthy subjects (d–f). The yellow circle highlights the region indicative of CAD in sub-images (a–c). Figure reproduced with permission from [26]

Fig. 2

Implemented model architecture

Fig. 3

Original images from the sick dataset. a is Axial-view b is Sagittal-view while c shows a Coronal view of a chest MRI scan (one frame)

Fig. 4

Heatmaps generated by GradCAM on test images. The most important features of the images that contribute to the classification of the image into certain classes are shown in darker colours. The three images are original, heatmap, and superimposed image

Fig. 5

Heatmaps generated by LRP on test images. The left column has original images, the middle column is the output heatmaps of LRP0 while the right column is the output heatmaps of LRP_Epsilon Technique