Detection and Classification of Lung Nodule in Diagnostic CT: A TsDN method based on Improved 3D-Faster R-CNN and Multi-Scale Multi-Crop Convolutional Neural Network

Abstract

Lung nodule classification has been one of the major problem relevant to Computer-Aided Diagnosis (CAD) system. Lung cancer for both men and women has been one of the leading causes of cancer related death. Deep learning models have produced promising performance in recent years, outperforming traditional methods in different fields. Nowadays, scientists have attempted numerous deep learning approaches to enhance the efficiency of CAD systems via Computed Tomography (CT) in lung cancer screening. In this paper, we presented a completely automatic lung CT system for cancer diagnosis named Two-step Deep Network (TsDN) and it contains two parts detection of nodule and classification. First, Improved 3D-Faster R-CNN with U-net like encoder and decoder is used for detection of nodule and then Multi-scale Multi-crop Convolutional Neural Network (MsMc-CNN) is proposed for the pulmonary nodule classification. The multi scale approach uses filters of various sizes to extract nodule features more efficiently from the local regions, and then multi crop pooling technique involves in extracting the important nodule information that cultivates various regions from convolutional feature map and then add numerous times for the maximum pooling. The proposed TsDN is trained and evaluated on LIDC-IDRI public dataset and achieved a sensitivity of 0.885 and specificity of 0.922 with AUC of 0.946. U-Net-like encoder and decoder framework for the detection of lung nodule. The nodules found are then fed into classification part for the lung nodule classification. We use Multi-scale Multi-crop Convolutional Neural Network (MsMc-CNN) to extract features for classification. To know more efficiently about local structures, the suggested MsMc-CNN uses convolutional multi scale layers to obtain features at various scales, we also demonstrate that with the multi crop pooling approach, the trained deep features were capable of capturing nodule salient details. Finally, our model is fully trained to classify the lung nodule into benign and malignant. The experimental result on LUNA16 and LIDC-IDRI show the enhanced performance of proposed TsDN system.