A novel receptive field‐regularized <scp>V‐net</scp> and nodule classification network for lung nodule detection
Shubham Dodia, B. Annappa, P A Mahesh
Abstract
Abstract Recent advancements in deep learning have achieved great success in building a reliable computer‐aided diagnosis (CAD) system. In this work, a novel deep‐learning architecture, named receptive field regularized V‐net (RFR V‐Net), is proposed for detecting lung cancer nodules with reduced false positives (FP). The method uses a receptive regularization on the encoder block's convolution and deconvolution layer of the decoder block in the V‐Net model. Further, nodule classification is performed using a new combination of SqueezeNet and ResNet, named nodule classification network (NCNet). Postprocessing image enhancement is performed on the 2D slice by increasing the image's intensity by adding pseudo‐color or fluorescence contrast. The proposed RFR V‐Net resulted in dice similarity coefficient of 95.01% and intersection over union of 0.83, respectively. The proposed NCNet achieved the sensitivity of 98.38% and FPs/Scan of 2.3 for 3D representations. The proposed NCNet resulted in considerable improvements over existing CAD systems.