Tensor-RT-Based Transfer Learning Model for Lung Cancer Classification.

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.