Scholarlink

Abstract <p>Improving patient outcomes requires liver cancer to be detected early, but challenges like data scarcity, imbalance, and model complexity hinder consistent high accuracy. This research proposes a novel data augmentation strategy using generative adversarial networks (GANs) to create synthetic tumor cases and enhance model robustness. The dynamic reduction with hierarchical feature aggregation (DRHFA) method is introduced to overcome high-dimensional data challenges. DRHFA employs adaptive recursive feature elimination (RFE) with harmony search optimization to dynamically reduce dimensions, preserving critical information efficiently. Hierarchical feature aggregation, implemented with attentive residual stacked neural network (AR-SNN), captures complex tumor characteristics at various abstraction levels for efficient feature extraction. AR-SNN integrates attention mechanisms, residual layers, and stacked autoencoders to address early liver tumor prediction challenges. It achieves remarkable accuracy with low complexity, presenting a promising solution for early liver tumor prediction in healthcare and oncology. The result reveals that the analysis carried out using Liver tumor detection dataset, the proposed method attained high accuracy of 0.99, low error as 0.0001, obtained high IoU as 99.8 and Dice score as 99.4 when compared to existing methods.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Attentive Residual Stacked Neural Network with DRHFA for Early Tumor Detection

  • 期刊论文

  • 发表日期：

    2025年3月1日

查看全文
  • Priti V. Kale,
  • Narendra M. Kandoi

摘要

Abstract

Improving patient outcomes requires liver cancer to be detected early, but challenges like data scarcity, imbalance, and model complexity hinder consistent high accuracy. This research proposes a novel data augmentation strategy using generative adversarial networks (GANs) to create synthetic tumor cases and enhance model robustness. The dynamic reduction with hierarchical feature aggregation (DRHFA) method is introduced to overcome high-dimensional data challenges. DRHFA employs adaptive recursive feature elimination (RFE) with harmony search optimization to dynamically reduce dimensions, preserving critical information efficiently. Hierarchical feature aggregation, implemented with attentive residual stacked neural network (AR-SNN), captures complex tumor characteristics at various abstraction levels for efficient feature extraction. AR-SNN integrates attention mechanisms, residual layers, and stacked autoencoders to address early liver tumor prediction challenges. It achieves remarkable accuracy with low complexity, presenting a promising solution for early liver tumor prediction in healthcare and oncology. The result reveals that the analysis carried out using Liver tumor detection dataset, the proposed method attained high accuracy of 0.99, low error as 0.0001, obtained high IoU as 99.8 and Dice score as 99.4 when compared to existing methods.