计算机科学
人工智能
随机森林
水准点(测量)
集合预报
接收机工作特性
人工神经网络
模式识别(心理学)
深度学习
二元分类
集成学习
机器学习
支持向量机
大地测量学
地理
作者
Sakshiwala,Maheshwari Prasad Singh
标识
DOI:10.1177/09544119231182037
摘要
Lung cancer is the uncontrolled growth of cells that originates in the lung parenchyma or cells that line the air passages. These cells divide rapidly to form malicious tumors. This paper proposes a multi-task ensemble of three dimensional (3D) deep neural network (DNN) based model, namely: pre-trained EfficientNetB0, BiGRU-based SEResNext101, and the proposed LungNet. The ensemble model performs binary classification and regression tasks to accurately classify the benign and malignant pulmonary nodules. This study also explores the attribute importance and proposes a domain knowledge-based regularization technique. The proposed model is evaluated on the public benchmark LIDC-IDRI dataset. Through a comparative study, it was shown that when coefficients generated by the random forest (RF) are used in the loss function, the proposed ensemble model offers a better prediction capability of the accuracy of 96.4% compared to the state-of-the-art methods. In addition, the receiver operating characteristic curves show that the proposed ensemble model has better performance than the base learners. Thus, the proposed CAD-based model can efficiently detect malignant pulmonary nodules.
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