Alsheikhy Ahmed A, Said Yahia, Shawly Tawfeeq, Alzahrani A Khuzaim, Lahza Husam
Department of Electrical Engineering, College of Engineering, Northern Border University, Arar 91431, Saudi Arabia.
Department of Electrical Engineering, Faculty of Engineering at Rabigh, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Diagnostics (Basel). 2023 Mar 19;13(6):1174. doi: 10.3390/diagnostics13061174.
Lung cancer starts and spreads in the tissues of the lungs, more specifically, in the tissue that forms air passages. This cancer is reported as the leading cause of cancer deaths worldwide. In addition to being the most fatal, it is the most common type of cancer. Nearly 47,000 patients are diagnosed with it annually worldwide. This article proposes a fully automated and practical system to identify and classify lung cancer. This system aims to detect cancer in its early stage to save lives if possible or reduce the death rates. It involves a deep convolutional neural network (DCNN) technique, VGG-19, and another deep learning technique, long short-term memory networks (LSTMs). Both tools detect and classify lung cancers after being customized and integrated. Furthermore, image segmentation techniques are applied. This system is a type of computer-aided diagnosis (CAD). After several experiments on MATLAB were conducted, the results show that this system achieves more than 98.8% accuracy when using both tools together. Various schemes were developed to evaluate the considered disease. Three lung cancer datasets, downloaded from the Kaggle website and the LUNA16 grad challenge, were used to train the algorithm, test it, and prove its correctness. Lastly, a comparative evaluation between the proposed approach and some works from the literature is presented. This evaluation focuses on the four performance metrics: accuracy, recall, precision, and F-score. This system achieved an average of 99.42% accuracy and 99.76, 99.88, and 99.82% for recall, precision, and F-score, respectively, when VGG-19 was combined with LSTMs. In addition, the results of the comparison evaluation show that the proposed algorithm outperforms other methods and produces exquisite findings. This study concludes that this model can be deployed to aid and support physicians in diagnosing lung cancer correctly and accurately. This research reveals that the presented method has functionality, competence, and value among other implemented models.
肺癌起源于肺部组织并在其中扩散，更具体地说，是在形成气道的组织中。据报道，这种癌症是全球癌症死亡的主要原因。它不仅是最致命的癌症，也是最常见的癌症类型。全球每年有近47000名患者被诊断出患有肺癌。本文提出了一种用于识别和分类肺癌的全自动实用系统。该系统旨在尽早检测出癌症，尽可能挽救生命或降低死亡率。它涉及一种深度卷积神经网络（DCNN）技术，即VGG - 19，以及另一种深度学习技术，长短期记忆网络（LSTM）。这两种工具在经过定制和集成后用于检测和分类肺癌。此外，还应用了图像分割技术。该系统是一种计算机辅助诊断（CAD）类型。在MATLAB上进行了多次实验后，结果表明，当同时使用这两种工具时，该系统的准确率超过98.8%。为评估所考虑的疾病制定了各种方案。从Kaggle网站和LUNA16梯度挑战赛下载的三个肺癌数据集用于训练算法、测试算法并证明其正确性。最后，对所提出的方法与文献中的一些研究进行了比较评估。该评估侧重于四个性能指标：准确率、召回率、精确率和F值。当VGG - 19与LSTM结合使用时，该系统的平均准确率为99.42%，召回率、精确率和F值分别为99.76%、99.88%和99.82%。此外，比较评估结果表明，所提出的算法优于其他方法并产生了精确的结果。本研究得出结论，该模型可用于协助和支持医生正确、准确地诊断肺癌。该研究表明，所提出的方法在其他已实施的模型中具有功能性、能力和价值。
