Automated decision support system for lung cancer detection and classification via enhanced RFCN with multilayer fusion RPN

Detection of lung cancer at early stages is critical, radiologists read computed tomography (CT) images to prescribe follow-up treatment. The conventional method for detecting nodule presence in CT images is tedious. We propose an enhanced multidimensional Region-based Fully Convolutional Network (mRFCN) based automated decision support system for lung nodule detection and classification. The mRFCN is used as an image classifier backbone for feature extraction along with the novel multi-Layer fusion Region Proposal Network (mLRPN) with position-sensitive score maps (PSSM) being explored. We applied a median intensity projection to leverage three-dimensional information from CT scans and introduced deconvolutional layer to adopt proposed mLRPN in our architecture to automatically select potential region-of-interest. Our system has been trained and evaluated using LIDC dataset, and the experimental results showed the promising detection performance in comparison to the state-of-the-art nodule detection/classification methods, achieving a sensitivity of 98.1% and classification accuracy of 97.91%.