Scholarlink

<p>As per the report by the National Institutes of Health (NIH), it has been found that pneumonia has 16 times and 10 times more reported cases than cancer and HIV-AIDS, respectively. X-ray scans are used by radiologists to diagnose pneumonia. Early disease diagnosis may prevent disease progression and improve patients’ lifespan. This work proposes deep learning-based stacked ensemble models by using SqueezeNet and VGG16 feature extractors integrated with hybrid trio stacked classification models. The proposed models have been implemented on a pooled dataset collected from three different sources with variations in the data to extract the most optimal features. The hyperparameters of these models have been configured with varied values to achieve higher outcomes for better disease detection. The performance evaluation of the VGG16-based ensembled model exhibits an accuracy of 98.01%, a recall of 0.9769, a precision of 0.828, an AUC of 0.906, and an F1-score of 0.8963, whereas the SqueezeNet-based ensembled model provides an accuracy of 85.9%, a recall of 0.859, a precision of 0.814, an AUC of 0.908, and an F1-score of 0.814. It has been proved through extensive implementation that the VGG16-based ensemble model outperforms the SqueezeNet-based ensemble model for the early detection of pneumonia in clinical decision making.</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

VGG16 and SqueezeNet-Based Ensembled Models Integrated with Hybrid Trio Stacked Classifiers for Early Clinical Decision Making

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月19日

查看全文
  • Shagun Sharma,
  • Kalpna Guleria

摘要

As per the report by the National Institutes of Health (NIH), it has been found that pneumonia has 16 times and 10 times more reported cases than cancer and HIV-AIDS, respectively. X-ray scans are used by radiologists to diagnose pneumonia. Early disease diagnosis may prevent disease progression and improve patients’ lifespan. This work proposes deep learning-based stacked ensemble models by using SqueezeNet and VGG16 feature extractors integrated with hybrid trio stacked classification models. The proposed models have been implemented on a pooled dataset collected from three different sources with variations in the data to extract the most optimal features. The hyperparameters of these models have been configured with varied values to achieve higher outcomes for better disease detection. The performance evaluation of the VGG16-based ensembled model exhibits an accuracy of 98.01%, a recall of 0.9769, a precision of 0.828, an AUC of 0.906, and an F1-score of 0.8963, whereas the SqueezeNet-based ensembled model provides an accuracy of 85.9%, a recall of 0.859, a precision of 0.814, an AUC of 0.908, and an F1-score of 0.814. It has been proved through extensive implementation that the VGG16-based ensemble model outperforms the SqueezeNet-based ensemble model for the early detection of pneumonia in clinical decision making.