A Multi-Model Deep Learning Framework and Algorithms for Survival Rate Prediction of Lung Cancer Subtypes With Region of Interest Using Histopathology Imagery

Lung cancer has been causing death at alarming rates across the globe. Identification of cancer subtypes and prediction of patient survival rate can significantly enhance treatment management. The existing methodologies on the two aspects mentioned above have limitations in terms of accuracy. In this paper, we proposed a multi-model deep learning framework and algorithms for cancer subtype classification and survival analysis. The framework has two pipelines with deep learning techniques for lung cancer type identification and survival analysis, respectively. An enhanced Convolutional Neural Network (CNN) model known as LCSCNet is proposed to detect lung cancer subtypes automatically. We proposed a deep learning model known as LCSANet for survival analysis by enhancing the VGG16 model. We proposed two algorithms to realize the proposed framework. The first algorithm, Learning Subtype Classification (LbSC), is based on LCSCNet. In contrast, the second algorithm, Learning Survival Analysis (LbSA), is based on LCSANet, which exploits Region of Interest (ROI) computation for efficiency in survival analysis. Our empirical study using the lung histopathology dataset and Cancer Genome Atlas lung cancer dataset revealed that the proposed deep learning models outperformed many existing models regarding type identification and survival analysis. The LCSCNet model could achieve 96.55% accuracy, while the LCSANet model could achieve 95.85%. Therefore, the proposed system can be incorporated into a real-world healthcare application for automatic lung cancer diagnosis and survival analysis.