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

Journal of Real-Time Image Processing (2024) 21:31 https://doi.org/10.1007/s11554-023-01411-7 RESEARCH Explaining decisions of a light‑weight deep neural network for real‑time coronary artery disease classification in magnetic resonance imaging Talha Iqbal1 · Aaleen Khalid2 · Ihsan Ullah1,2 Received: 28 September 2023 / Accepted: 28 December 2023 / Published online: 10 February 2024 © The Author(s) 2024 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 Healthcare models · Time complexity · Hyper-parameter tuning · Explainable AI · Classification 1 Introduction According to the World Health Organization1, in 2019, an estimated 17.9 million people died from cardiovascular diseases, representing 32% of all global deaths. Statistics published by the American Heart Association in 2023 state that from 2017-2020, an estimated 20.5 million Americans had * Ihsan Ullah Talha Iqbal Aaleen Khalid 1 Insight SFI Research Centre for Data Analytics, University of Galway, Galway H91 TK33, Ireland 2 School of Computer Science, University of Galway, Galway H91 TK33, Ireland coronary heart disease (CHD) [1]. Specifically, Coronary artery disease (CAD) accounts for approximately 610,000 deaths annually in the United States and is the third leading cause of death worldwide, with 17.8 million deaths annually [2]. The patient’s symptoms of CAD are neither sensitive nor specific, thus making it difficult for clinicians or cardiologists to rely only on them. The reference standard for CAD detection is coronary angiography, which is an invasive diagnostic imaging procedure performed using cardiac catheterization [3]. This method is expensive and carries potential risks. Other methods include cardiac imaging techniques, which are safe, non-invasive, cheaper and can help doctors in early detection and providing timely interventions to treat CAD patients. These techniques include X-rays, Computer Tomography (CT), Echo-cardiogram and Magnetic 1 https://www.who.int/news-room/fact-sheets/detail/cardiovascular- diseases-%28cvds%29. Vol.:(0123456789) 31 Page 2 of 11 Resonance Imaging (MRI) or Cardiac Magnetic Resonance (CMR) Imaging [4]. X-rays and CT imaging techniques use ionizing radiations, which are considered harmful if a patient is overexposed to them [5]. Echocardiograms are limited by cost, time, and acoustic window access [6]. MRI or CMR imaging uses magnetic waves and is considered a viable alternative for non-invasive assessment of CAD [7]. MRI/CMR images provide precise measurements of heart structure and functions, as well as myocardial perfusion and parametric quantification. MRI/CMR could be 2D or 3D, but 3D imaging has excessive artifacts and has thus not been clinically used for the diagnosis of CAD [8]. Manual interpretation of 2D scans is also time-consuming and requires experience. Thus, artificial intelligence methods are exploited to automate the CAD diagnosis to reduce the analysis time with potentially improved accuracy. This plays a critical role in connected healthcare settings (transitioning healthcare services remotely, from hospitals to patient side or home-based care). However, there are several challenges in implementing such AI models on computational tools such as Field Programmable Gate Arrays (FPGAs), Raspberry Pi and central processing unit (CPU)/graphics processing unit (GPU) based systems. These challenges arise due to the limited processing power, memory, and energy efficiency of these devices. It is essential to engage in a multidisciplinary approach that involves collaboration between domain experts, data scientists and hardware engineers to overcome these challenges. Convolutional Neural Network (CNN) models have yielded unprecedented achievements in addressing computer vision challenges, including but not limited to image classification, object detection, and tracking. Nonetheless, their integration into embedded applications has been impeded by the substantial computational and memory requisites, thereby giving rise to a novel research domain known as model compression including bit reduction, knowledge distillation, tensor decomposition, network pruning, and microarchitecture [9]. Interested readers are referred to [10] for detailed insights, advantages and limitations of each mentioned method. While these strategies have demonstrated notable achievements, they are not without their inherent constraints. This paper introduces a lightweight Convolutional Neural Network (CNN) model designed specifically for real-time implementation as a classifier. In connected healthcare settings, where low latency and efficient processing are crucial, this lightweight CNN offers a promising solution. By optimizing the model’s architecture and parameters, we aim to strike a balance between computational efficiency and classification accuracy, enabling real-time CAD detection. This approach has great potential to improve the deployment of AI systems in resource constrained environments, ultimately benefiting the overall healthcare systems. Journal of Real-Time Image Processing (2024) 21:31 The remaining paper is organised as; Sect. 2 summaries the available literature on real-time CAD classification networks, Sect. 3 highlights the proposed work and dataset description, Sect. 4 provides calculations and the experimental results and the conclusion and future work are presented (...truncated)