Lung Nodule Classification
8 papers with code • 1 benchmarks • 1 datasets
DeepLung consists of two components, nodule detection (identifying the locations of candidate nodules) and classification (classifying candidate nodules into benign or malignant).
Multi-level CNN for lung nodule classification with Gaussian Process assisted hyperparameter optimization
In this paper, a non-stationary kernel is proposed which allows the surrogate model to adapt to functions whose smoothness varies with the spatial location of inputs, and a multi-level convolutional neural network (ML-CNN) is built for lung nodule classification whose hyperparameter configuration is optimized by using the proposed non-stationary kernel based Gaussian surrogate model.
In this paper, we propose a novel method to predict the malignancy of nodules that have the capability to analyze the shape and size of a nodule using a global feature extractor, as well as the density and structure of the nodule using a local feature extractor.
Lung malignancy is one of the most common causes of death in the world caused by malignant lung nodules which commonly diagnosed radiologically by radiologists.
Learning Efficient, Explainable and Discriminative Representations for Pulmonary Nodules Classification
Besides, empirical study shows that the reasoning process of learned networks is in conformity with physicians' diagnosis.