首页    期刊浏览 2024年11月06日 星期三
登录注册

文章基本信息

  • 标题:A multimodal deep learning infused with artificial algae algorithm – An architecture of advanced E-health system for cancer prognosis prediction
  • 本地全文:下载
  • 作者:Summrina Kanwal ; Faiza Khan ; Sultan Alamri
  • 期刊名称:Journal of King Saud University @?C Computer and Information Sciences
  • 印刷版ISSN:1319-1578
  • 出版年度:2022
  • 卷号:34
  • 期号:6
  • 页码:2707-2719
  • 语种:English
  • 出版社:Elsevier
  • 摘要:Cancer becomes life-threatening when it affects vital organs and their potential to function; therefore, early discovery is crucial for prognosis management. Medical research benefits from the rapid growth of Artificial Intelligence (AI) technology, notably machine learning (ML), deep learning (DL), and reinforcement learning (RL) which has resulted in the development of several approaches and techniques for improving cancer prediction and survival analysis. To improve cancer prognosis prediction and survival analysis, a novel framework that fuses multimodal features via early and late fusion techniques using DL and RL is presented. The approach utilizes an artificial algae algorithm (AAA) to extract the most resilient features from the dataset and build a robust model for cancer prognosis and prediction. The uniqueness of this technique is illustrated by using AAA to extract valuable features from a variety of data modalities infused with Double DEEP Q-NETWORK (DDQN), Convolution eXtreme Gradient Boosting (CNN-XGBOOST), and Convolution Support Vector Machine (CNN-SVM) models for cancer prognostic prediction. The effectiveness of the proposed framework was evaluated using publicly-available datasets: Lower Grade Glioma in the Brain(BRAIN-TCGA), Prostate Cancer, Bladder Cancer, Metastatic Colorectal Cancer (MSKCC), and the METABRIC dataset. The optimum accuracy achieved was 0.99 for the BRAIN-TCGA dataset, surpassing a variety of state-of-the-art techniques. The performance assessment results reveal that the proposed model outperforms currently available cancer prognostic prediction models in terms of predictive performance.
国家哲学社会科学文献中心版权所有