LDNNET: Towards Robust Classification of Lung Nodule and Cancer Using Lung Dense Neural Network

Lung nodule classification plays an important role in diagnosis of lung cancer which is essential to patients' survival. However, because the number of lung CT images in current dataset is relatively small and the ratio of nodule samples to non-nodule samples is usually very different, this makes the training of neural networks difficult and poor performance of neural networks. Hence, LDNNET is proposed, which adopts Dense-Block, batch normalization (BN) and dropout to cope with these problems. Meanwhile, LDNNET is an adaptive architecture based on convnets combining softmax classifier which is utilized to alleviate the problems of training deep convnets. Follows are our main work: Firstly, we utilized LDNNET on database LUng Nodule Analysis 2016 (LUNA16) for lung nodule classification and database KAGGLE DATA-SCIENCE-BOWL-2017(Kaggle DSB 2017) for lung cancer classification; Secondly, the comparison experiments are designed to compare the performance of dense connection, pooling layer and the input pixel size of lung CT(Computed Tomography) images; Thirdly, data enhancement, dense connection and dropout layer were utilized in LDNNET to reduce overfitting; Fourthly, pre-processing methods, for instance enhanced contrast, median filtering, Laplacian filtering are compared to the no-processing method to explore the effect of pre-processing on lung CT images classification. Fifthly, accuracy, specificity and sensitivity on LUNA16 are 0.988396, 0.994585 and 0.982072 and these indicators on Kaggle DSB 2017 are 0.999480, 0.999652 and 0.998974. Furthermore, AUC for both two datasets is over 0.98. Consequently, this paper conducts experiments with uniform parameter settings on two publicly available databases and shows that even in challenging situation where lung images are directly utilized as input images without preprocessing, LDNNET is still the more advanced algorithm than other recent algorithms respectively. Moreover, a series of comparative experiments were conducted to further confirm that the proposed algorithm has the higher accuracy and robustness through verification and discussion.