Multiscale CNN with compound fusions for false positive reduction in lung nodule detection

Abstract

Pulmonary lung nodules are often benign at the early stage but they could easily become malignant and metastasize to other locations in later stages. Morphological characteristics of these nodule instances vary largely in terms of their size, shape, and texture. There are also other co-existing lung anatomical structures such as lung walls and blood vessels surrounding these nodules resulting in complex contextual information. As a result, their early diagnosis to enable decisive intervention using Computer-Aided Diagnosis (CAD) systems face serious challenges, especially at low false positive rates. In this paper, we propose a new Convolutional Neural Network (CNN) architecture called Multiscale CNN with Compound Fusions (MCNN-CF) for this purpose which uses multiscale 3D patches as inputs and performs a fusion of intermediate features at two different depths of the network in two diverse fashions. The network is trained by a new iterative training procedure adapted to circumvent the class imbalance problem and obtained a Competitive Performance Metric (CPM) score of 0.948 when tested on the LUNA16 dataset. Experimental results illustrate the robustness of the proposed system which has increased the confidence of the prediction probabilities in the detection of the most variety of nodules.