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

作　　者：A.Robert Singh Suganya Athisayamani Gyanendra Prasad Joshi Bhanu Shrestha 

机构地区：[1]Department of Computational Intelligence,SRM Institute of Science and Technology,Kattankulathur,603203,Tamil Nadu,India [2]School of Computing,Sastra Deemed to be University,Thanjavur,613401,Tamil Nadu,India [3]Department of Artificial Intelligence&Software,Kangwon National University,Samcheok,25913,Republic of Korea [4]Department of Information Convergence System,Graduate School of Smart Convergence,Kwangwoon University,Seoul,01897,Republic of Korea

出　　处：《Computer Modeling in Engineering & Sciences》2025年第1期299-327,共29页工程与科学中的计算机建模（英文）

基　　金：the Research Grant of Kwangwoon University in 2024.

摘　　要：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 3×3 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.

关 键 词：SPECT-MPI CAD MSDC DENOISING attenuation correction classification 

分 类 号：R541[医药卫生—心血管疾病] TP3[医药卫生—内科学]