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The recommended technique ended up being compared against state-of-the-art strategies in a dataset of 1376 CVD pictures. Experiments showed that the VENet achieved a classification performance of 96.4%, 96.4%, and 97.2% for reliability, precision, and recall, respectively, and a segmentation overall performance of 75.4%, 76.7.0%, 76.7% when it comes to Dice coefficient, accuracy, and recall, respectively. The shared formulation increased the robustness of both jobs when
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