Region Growing based K-means Clustering and Optimal Weight Prior-Attention Residual Learning for Segmentation and Classification of COVID-19 CT Images
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Abstract
COVID-19 has recently grown significantly over the globe, posing several obstacles for scholars. The first stage in COVID-19 and lung image analysis is lung image segmentation. Due to the intensity inhomogeneity, presence of artifacts, and proximity to the grey level of various soft tissues, the primary difficulties of segmentation algorithms were accentuated. However, the situation takes much more time than determining slice-by-slice to identify the lesions from volumetric chest CT images. Manual screening of COVID-19 using Computed Tomography (CT) images is arduous. The detection and type-classification sub-networks and the ultimately linked layers' learning weights are growing harder to select. Sole this issue, the proposed introduces Region Growing Based K-Means Clustering (RKMC) for segmentationresidual Learning with proposed Optimal Weight Prior-Attention Residual Learning (OWPARL) for classification of COVID-19 CT images. Stacking OWPARL blocks enables quick model construction and end-to-end multi-task loss training. Using lung scans of patients with and without pneumonia, one 3D-ResNet branch is particularly trained as a binary classifier to identify the lesion locations inside the lungs. The performance evolution metrics are precision, specificity, sensitivity, F-measure, and Area Under Curve (AUC) results compared to the RKMC+OWPARL procedure, which proposes greater correctness.
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https://www.kaggle.com/plameneduardo/sarscov2-ctscan-dataset?select=non-COVID