Multi-Scale Dilated Convolution Network for SPECT-MPI Cardiovascular Disease Classification with Adaptive Denoising and Attenuation Correction

Myocardial perfusion imaging (MPI), which uses single-photon emission computed tomography (SPECT), is a well-known estimating tool for medical diagnosis, employing the classification of images to show situations in coronary artery disease (CAD). The automatic classification of SPECT images for different techniques has achieved near-optimal accuracy when using convolutional neural networks (CNNs). This paper uses a SPECT classification framework with three steps: 1) Image denoising, 2) Attenuation correction, and 3) Image classification. Image denoising is done by a U-Net architecture that ensures effective image denoising. Attenuation correction is implemented by a convolution neural network model that can remove the attenuation that affects the feature extraction process of classification. Finally, a novel multi-scale diluted convolution (MSDC) network is proposed. It merges the features extracted in different scales and makes the model learn the features more efficiently. Three scales of filters with size 3 x 3 are used to extract features. All three steps are compared with state-of-the-art methods. The proposed denoising architecture ensures a high-quality image with the highest peak signal-to-noise ratio (PSNR) value of 39.7. The proposed classification method is compared with the five different CNN models, and the proposed method ensures better classification with an accuracy of 96%, precision of 87%, sensitivity of 87%, specificity of 89%, and F1-score of 87%. To demonstrate the importance of preprocessing, the classification model was analyzed without denoising and attenuation correction.