Khan Adeel, Tariq Irfan, Khan Haroon, Khan Sifat Ullah, He Nongyue, Zhiyang Li, Raza Faisal
State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China.
Department of Biotechnology, University of Science and Technology, Bannu, KP, Pakistan.
J Oncol. 2022 Sep 26;2022:5682451. doi: 10.1155/2022/5682451. eCollection 2022.
Lung cancer is the deadliest cancer killing almost 1.8 million people in 2020. The new cases are expanding alarmingly. Early lung cancer manifests itself in the form of nodules in the lungs. One of the most widely used techniques for both lung cancer early and noninvasive diagnosis is computed tomography (CT). However, the intensive workload of radiologists to read a large number of scans for nodules detection gives rise to issues like false detection and missed detection. To overcome these issues, we proposed an innovative strategy titled adaptive boosting self-normalized multiview convolution neural network (AdaBoost-SNMV-CNN) for lung cancer nodules detection across CT scans. In AdaBoost-SNMV-CNN, MV-CNN function as a baseline learner while the scaled exponential linear unit (SELU) activation function normalizes the layers by considering their neighbors' information and a special drop-out technique (-dropout). The proposed method was trained and tested using the widely Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) and Early Lung Cancer Action Program (ELCAP) datasets. AdaBoost-SNMV-CNN achieved an accuracy of 92%, sensitivity of 93%, and specificity of 92% for lung nodules detection on the LIDC-IDRI dataset. Meanwhile, on the ELCAP dataset, the accuracy for detecting lung nodules was 99%, sensitivity 100%, and specificity 98%. AdaBoost-SNMV-CNN outperformed the majority of the model in accuracy, sensitivity, and specificity. The multiviews confer the model's good generalization and learning ability for diverse features of lung nodules, the model architecture is simple, and has a minimal computational time of around 10 minutes. We believe that AdaBoost-SNMV-CNN has good accuracy for the detection of lung nodules and anticipate its potential application in the noninvasive clinical diagnosis of lung cancer. This model can be of good assistance to the radiologist and will be of interest to researchers involved in the designing and development of advanced systems for the detection of lung nodules to accomplish the goal of noninvasive diagnosis of lung cancer.
肺癌是最致命的癌症，2020年导致近180万人死亡。新病例数量正以惊人的速度增长。早期肺癌以肺部结节的形式出现。计算机断层扫描（CT）是肺癌早期和非侵入性诊断中使用最广泛的技术之一。然而，放射科医生为检测结节而阅读大量扫描图像的繁重工作量引发了诸如误检和漏检等问题。为了克服这些问题，我们提出了一种创新策略，即自适应增强自归一化多视图卷积神经网络（AdaBoost-SNMV-CNN），用于在CT扫描中检测肺癌结节。在AdaBoost-SNMV-CNN中，MV-CNN作为基线学习器，而缩放指数线性单元（SELU）激活函数通过考虑其相邻层的信息和一种特殊的随机失活技术（-dropout）对各层进行归一化。所提出的方法使用广泛使用的肺部图像数据库联盟和图像数据库资源倡议（LIDC-IDRI）以及早期肺癌行动计划（ELCAP）数据集进行训练和测试。AdaBoost-SNMV-CNN在LIDC-IDRI数据集上检测肺结节的准确率达到92%，灵敏度为93%，特异性为92%。同时，在ELCAP数据集上，检测肺结节的准确率为99%，灵敏度为100%，特异性为98%。AdaBoost-SNMV-CNN在准确率、灵敏度和特异性方面优于大多数模型。多视图赋予了该模型对肺结节多样特征的良好泛化和学习能力，模型架构简单，计算时间最短约为10分钟。我们相信AdaBoost-SNMV-CNN在检测肺结节方面具有良好的准确性，并期待其在肺癌非侵入性临床诊断中的潜在应用。该模型可为放射科医生提供很好的帮助，并且将引起参与设计和开发用于检测肺结节的先进系统以实现肺癌非侵入性诊断目标的研究人员的兴趣。
