Multi-Layered Model Approach (MLMA) For Heart Vein Blockage Detection Using Gated Recurrent Unit (GRU) and Denoising Autoencoders (DAES)

Abstract

Cardiovascular diseases (CVD) continue to be the leading cause of death worldwide, and sophisticated diagnostic techniques must be developed to detect and treat them early. This study analyzed vein blockages in the heart and proposed an ensemble model for the early diagnosis of heart problems. Venous blockages can be found using non-invasive imaging techniques, frequently suggesting cardiovascular abnormalities. Using a Coronary Computed Tomography Angiography (CCTA) dataset that includes normal and abnormal cardiac cases, the pre-trained 3D convolutional neural network (3D-CNN) model can learn complex patterns linked to early-stage cardiac problems. To improve the accuracy and dependability of the detection process, the proposed ensemble model incorporates several algorithms, including the segmentation-based threshold method, feature extraction-based form descriptors, and the vessel tracking algorithm (VTA). The ensemble model consisted of several base models, each focusing on a distinct area of vein blockage investigation. Feature extraction techniques extract pertinent information from medical imaging data, such as angiograms and vascular scans. The ensemble model, which uses the combined decision-making ability of several algorithms, including shape descriptors, is fed to the retrieved features. The proposed ensemble model performed better than the individual models and conventional diagnostic techniques regarding early detection accuracy. Moreover, analysis of the feature importance scores improves the interpretability of the model and sheds light on the crucial markers of cardiac anomalies. The suggested ensemble model has a significant potential clinical impact because early identification of cardiac problems enables prompt intervention and better patient outcomes. Finally, the proposed method combines the multi-layer approach with gated recurrent units (GRU) and a Denoising Autoencoder (DAE) to find abnormalities in angiography images. The effectiveness of the suggested method demonstrates a high rate of disease diagnosis and accuracy for specified angiography images.