Scholarlink

<p>Breast cancer prognosis prediction is pivotal to improving the survival chances of breast cancer patients. Recently, deep learning models integrated with multi-modal data have been explored extensively to improve the reliability and accuracy of breast cancer prognosis prediction. However, multi-modal data poses challenges to feature extraction due to the varied distribution and dimensionality across the modalities. To address this, we developed a multi-input convolutional neural network model, which extracts features from each modality in the multi-modal dataset simultaneously using separate convolutional layers, then concatenates and passes them to shared dense layers. The proposed model achieved area under the receiver operating characteristic curve values of 0.893 and 0.865 in 5-fold cross-validation and unseen test data respectively (at threshold = 0.2). These outcomes surpassed those of single-input convolutional neural network models and a state-of-the-art method based on multi-modal data. The multi-input convolutional neural network model efficiently handles multi-modal data and is a promising tool for breast cancer prognosis prediction.</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

Multi-input CNN: a deep learning-based approach for predicting breast cancer prognosis using multi-modal data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月17日

查看全文
  • Shamita Uma Kandan,
  • Mariam Mohamed Alketbi,
  • Zaher Al Aghbari

摘要

Breast cancer prognosis prediction is pivotal to improving the survival chances of breast cancer patients. Recently, deep learning models integrated with multi-modal data have been explored extensively to improve the reliability and accuracy of breast cancer prognosis prediction. However, multi-modal data poses challenges to feature extraction due to the varied distribution and dimensionality across the modalities. To address this, we developed a multi-input convolutional neural network model, which extracts features from each modality in the multi-modal dataset simultaneously using separate convolutional layers, then concatenates and passes them to shared dense layers. The proposed model achieved area under the receiver operating characteristic curve values of 0.893 and 0.865 in 5-fold cross-validation and unseen test data respectively (at threshold = 0.2). These outcomes surpassed those of single-input convolutional neural network models and a state-of-the-art method based on multi-modal data. The multi-input convolutional neural network model efficiently handles multi-modal data and is a promising tool for breast cancer prognosis prediction.