Liver Tumor Prediction with Advanced Attention Mechanisms Integrated
  into a Depth-Based Variant Search Algorithm
P. Kalaiselvi S. Anusuya
In recent days, Deep Learning (DL) techniques have become an emerging
transformation in the field of machine learning, artificial intelligence,
computer vision, and so on. Subsequently, researchers and industries have been
highly endorsed in the medical field, predicting and controlling diverse
diseases at specific intervals. Liver tumor prediction is a vital chore in
analyzing and treating liver diseases. This paper proposes 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 work aims to improve accuracy and robustness in
diagnosing and treating liver diseases. The anticipated model is assessed on a
Computed Tomography (CT) scan dataset containing both benign and malignant
liver tumors. The proposed approach achieved high accuracy in predicting liver
tumors, outperforming other state-of-the-art methods. Additionally, advanced
attention mechanisms were incorporated into the CNN model to enable the
identification and highlighting of regions of the CT scans most relevant to
predicting liver tumors. The results suggest that incorporating attention
mechanisms and a depth-based variant search algorithm into the CNN model is a
promising approach for improving the accuracy and robustness of liver tumor
prediction. It can assist radiologists in their diagnosis and treatment
planning. The proposed system achieved a high accuracy of 95.5% in predicting
liver tumors, outperforming other state-of-the-art methods.