Bhattacharjee Ananya, Murugan R, Soni Badal, Goel Tripti
Bio-Medical Imaging Laboratory (BIOMIL), Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Silchar, Assam, 788010, India.
Department of Computer Science and Engineering, National Institute of Technology Silchar, Silchar, Assam, 788010, India.
Phys Eng Sci Med. 2022 Sep;45(3):981-994. doi: 10.1007/s13246-022-01150-2. Epub 2022 Jun 30.
Lung cancer is considered one of the leading causes of death all across the world. Various radiology-related fields increasingly have used Computer-aided diagnosis (CAD) systems. It just has already become a part of clinical work for lung cancer detection. In this article, we proposed an Adaptive Boost-based Grid Search Optimized Random Forest (Ada-GridRF) classifier that best optimized the hyperparameters of the base random forest model to identify the malignant and non-malignant nodules from the trained CT images. Improved performance speed and reduced computational complexity were the advantages of the proposed method. The proposed methodology was compared with other hyperparameter optimization techniques and also with different conventional approaches. It even outperformed the popular state-of-the-art deep learning techniques such as transfer learning and convolutional neural network. The experimental results proved that the proposed method yielded the best performance metrics of 97.97% accuracy, 100% sensitivity, 96% specificity, 96.08% precision, 98% F1-score, 4% False positives rate, and 99.8% Area under the ROC curve (AUC). It took only 8 msec to train the model. Thus, the proposed Ada-GridRF model can aid radiologists in fast lung cancer detection.
肺癌被认为是全球主要的死亡原因之一。各个与放射学相关的领域越来越多地使用计算机辅助诊断（CAD）系统。它已然成为肺癌检测临床工作的一部分。在本文中，我们提出了一种基于自适应增强的网格搜索优化随机森林（Ada - GridRF）分类器，该分类器对基础随机森林模型的超参数进行了最佳优化，以从训练的CT图像中识别恶性和非恶性结节。提高性能速度和降低计算复杂度是所提方法的优点。将所提方法与其他超参数优化技术以及不同的传统方法进行了比较。它甚至优于诸如迁移学习和卷积神经网络等流行的先进深度学习技术。实验结果证明，所提方法产生了最佳性能指标，准确率为97.97%，灵敏度为100%，特异性为96%，精确率为96.08%，F1分数为98%，误报率为4%，ROC曲线下面积（AUC）为99.8%。训练该模型仅需8毫秒。因此，所提的Ada - GridRF模型可以帮助放射科医生快速检测肺癌。
