Indira Gandhi Delhi Technical University for Women, Delhi, India.
J Digit Imaging. 2023 Aug;36(4):1364-1375. doi: 10.1007/s10278-023-00822-z. Epub 2023 Apr 14.
Cancer is a leading cause of death across the globe, in which lung cancer constitutes the maximum mortality rate. Early diagnosis through computed tomography scan imaging helps to identify the stages of lung cancer. Several deep learning-based classification methods have been employed for developing automatic systems for the diagnosis and detection of computed tomography scan lung slices. However, the diagnosis based on nodule detection is a challenging task as it requires manual annotation of nodule regions. Also, these computer-aided systems have yet not achieved the desired performance in real-time lung cancer classification. In the present paper, a high-speed real-time transfer learning-based framework is proposed for the classification of computed tomography lung cancer slices into benign and malignant. The proposed framework comprises of three modules: (i) pre-processing and segmentation of lung images using K-means clustering based on cosine distance and morphological operations; (ii) tuning and regularization of the proposed model named as weighted VGG deep network (WVDN); (iii) model inference in Nvidia tensor-RT during post-processing for the deployment in real-time applications. In this study, two pre-trained CNN models were experimented and compared with the proposed model. All the models have been trained on 19,419 computed tomography scan lung slices, which were obtained from the publicly available Lung Image Database Consortium and Image Database Resource Initiative dataset. The proposed model achieved the best classification metric, an accuracy of 0.932, precision, recall, an F1 score of 0.93, and Cohen's kappa score of 0.85. A statistical evaluation has also been performed on the classification parameters and achieved a p-value <0.0001 for the proposed model. The quantitative and statistical results validate the improved performance of the proposed model as compared to state-of-the-art methods. The proposed framework is based on complete computed tomography slices rather than the marked annotations and may help in improving clinical diagnosis.
癌症是全球主要的死亡原因，其中肺癌的死亡率最高。通过计算机断层扫描成像进行早期诊断有助于确定肺癌的阶段。已经采用了几种基于深度学习的分类方法来开发用于诊断和检测计算机断层扫描肺切片的自动系统。然而，基于结节检测的诊断是一项具有挑战性的任务，因为它需要手动注释结节区域。此外，这些计算机辅助系统在实时肺癌分类方面尚未达到预期的性能。在本文中，提出了一种基于高速实时迁移学习的框架，用于将计算机断层扫描肺癌切片分类为良性和恶性。该框架包括三个模块：（i）使用基于余弦距离和形态学操作的 K-均值聚类对肺图像进行预处理和分割；（ii）调整和正则化名为加权 VGG 深度网络（WVDN）的提出模型；（iii）在 Nvidia tensor-RT 中进行模型推断，以便在实时应用中部署。在这项研究中，实验了两个预训练的 CNN 模型，并与所提出的模型进行了比较。所有模型都在 19419 个计算机断层扫描肺切片上进行了训练，这些切片来自公开的 Lung Image Database Consortium 和 Image Database Resource Initiative 数据集。所提出的模型实现了最佳的分类指标，准确性为 0.932、精度、召回率、F1 得分为 0.93，Cohen's kappa 得分为 0.85。还对分类参数进行了统计评估，并为所提出的模型获得了 p 值<0.0001。定量和统计结果验证了与最先进的方法相比，所提出的模型的性能得到了提高。所提出的框架基于完整的计算机断层扫描切片，而不是标记的注释，可能有助于改善临床诊断。
