Dodia, S.Annappa, B.Mahesh, M.2026-02-052022International Journal of Imaging Systems and Technology, 2022, 32, 1, pp. 88-1018999457https://doi.org/10.1002/ima.22636https://idr.nitk.ac.in/handle/123456789/22886Recent 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. © 2021 Wiley Periodicals LLC.Biological organsComputer aided diagnosisComputer aided instructionImage enhancement3d representationsClassification networksComputer Aided Diagnosis(CAD)False positiveLearning architecturesLung nodule detectionReceptive fieldsSimilarity coefficientsDeep learning