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A recent study conducted by researchers at the Saveetha School of Engineering in Tamil Nadu, India, has proposed a novel approach for predicting liver tumors using Convolutional Neural Networks (CNN) and a depth-based variant search algorithm with advanced attention mechanisms (CNN-DS-AM). The proposed approach achieved high accuracy in predicting liver tumors, outperforming other state-of-the-art methods. The incorporation of attention mechanisms and a depth-based variant search algorithm into the CNN model shows promise in improving the accuracy and robustness of liver tumor prediction, which can assist radiologists in diagnosis and treatment planning. The research has been peer-reviewed and published in Computers Materials & Continua. [Extracted from the article]