scholarly journals Segmentation of Lung Nodules Using Improved 3D-UNet Neural Network

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1787
Author(s):  
Zhitao Xiao ◽  
Bowen Liu ◽  
Lei Geng ◽  
Fang Zhang ◽  
Yanbei Liu
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Image Segmentation ◽  
Deep Level ◽  
Lung Nodule ◽  
Recall Rate ◽  
Lung Nodules ◽  
Multi Scale ◽  
Diagnosis Of Lung Cancer ◽  
Computed Tomography Image

Lung cancer has one of the highest morbidity and mortality rates in the world. Lung nodules are an early indicator of lung cancer. Therefore, accurate detection and image segmentation of lung nodules is of great significance to the early diagnosis of lung cancer. This paper proposes a CT (Computed Tomography) image lung nodule segmentation method based on 3D-UNet and Res2Net, and establishes a new convolutional neural network called 3D-Res2UNet. 3D-Res2Net has a symmetrical hierarchical connection network with strong multi-scale feature extraction capabilities. It enables the network to express multi-scale features with a finer granularity, while increasing the receptive field of each layer of the network. This structure solves the deep level problem. The network is not prone to gradient disappearance and gradient explosion problems, which improves the accuracy of detection and segmentation. The U-shaped network ensures the size of the feature map while effectively repairing the lost features. The method in this paper was tested on the LUNA16 public dataset, where the dice coefficient index reached 95.30% and the recall rate reached 99.1%, indicating that this method has good performance in lung nodule image segmentation.

scholarly journals Multi-Level Cross Residual Network for Lung Nodule Classification

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2837 ◽  
Author(s):  
Juan Lyu ◽  
Xiaojun Bi ◽  
Sai Ho Ling
Keyword(s):  
Lung Cancer ◽  
Early Stage ◽  
Binary Classification ◽  
Lung Nodule ◽  
Disease Diagnosis ◽  
Lung Nodules ◽  
Lung Cancer Patients ◽  
Multi Scale ◽  
Multi Level ◽  
Nodule Classification

Computer-aided algorithm plays an important role in disease diagnosis through medical images. As one of the major cancers, lung cancer is commonly detected by computer tomography. To increase the survival rate of lung cancer patients, an early-stage diagnosis is necessary. In this paper, we propose a new structure, multi-level cross residual convolutional neural network (ML-xResNet), to classify the different types of lung nodule malignancies. ML-xResNet is constructed by three-level parallel ResNets with different convolution kernel sizes to extract multi-scale features of the inputs. Moreover, the residuals are connected not only with the current level but also with other levels in a crossover manner. To illustrate the performance of ML-xResNet, we apply the model to process ternary classification (benign, indeterminate, and malignant lung nodules) and binary classification (benign and malignant lung nodules) of lung nodules, respectively. Based on the experiment results, the proposed ML-xResNet achieves the best results of 85.88% accuracy for ternary classification and 92.19% accuracy for binary classification, without any additional handcrafted preprocessing algorithm.

scholarly journals Classification of Lung Nodules using Improved Residual Convolutional Neural Network

2020 ◽  
Vol 1 (1) ◽  
pp. 15-21
Author(s):  
Salah Eldeen Babiker ◽  
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Early Stage ◽  
Lung Nodule ◽  
Early Stage Lung Cancer ◽  
Evolutionary Neural Network ◽  
Diagnosis Of Lung Cancer ◽  
Experimental Findings ◽  
Lung Cancer Detection

The most common cancer of the lung cannot be ignored and can cause late-health death. Now CT can be used to help clinicians diagnose early-stage lung cancer. In certain cases the diagnosis of lung cancer detection is based on doctors' intuition, which can neglect other patients and cause complications. Deep learning in most other areas of medical diagnosis has proven to be a common and powerful tool. This research is planned for improving the residual evolutionary neural network (IRCNN). These networks apply with some changes to the benign and malignant lung nodule to the CT image classification task. The segmenting of the nodule is performed here by clustering k-means. The LIDC-IDRI database analysed those networks. Experimental findings show that the IRCNN network archived the best performance of lung nodule classification, which findings best among established methods.

scholarly journals An Efficient DA-Net Architecture for Lung Nodule Segmentation

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1457
Author(s):  
Muazzam Maqsood ◽  
Sadaf Yasmin ◽  
Irfan Mehmood ◽  
Maryam Bukhari ◽  
Mucheol Kim
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Ct Scan ◽  
Cancer Diagnosis ◽  
Lung Nodule ◽  
Search Space ◽  
Lung Nodules ◽  
Suggested Model ◽  
Lung Cancer Diagnosis ◽  
Segmentation Framework

A typical growth of cells inside tissue is normally known as a nodular entity. Lung nodule segmentation from computed tomography (CT) images becomes crucial for early lung cancer diagnosis. An issue that pertains to the segmentation of lung nodules is homogenous modular variants. The resemblance among nodules as well as among neighboring regions is very challenging to deal with. Here, we propose an end-to-end U-Net-based segmentation framework named DA-Net for efficient lung nodule segmentation. This method extracts rich features by integrating compactly and densely linked rich convolutional blocks merged with Atrous convolutions blocks to broaden the view of filters without dropping loss and coverage data. We first extract the lung’s ROI images from the whole CT scan slices using standard image processing operations and k-means clustering. This reduces the search space of the model to only lungs where the nodules are present instead of the whole CT scan slice. The evaluation of the suggested model was performed through utilizing the LIDC-IDRI dataset. According to the results, we found that DA-Net showed good performance, achieving an 81% Dice score value and 71.6% IOU score.

scholarly journals Application of Deep Learning in Lung Cancer Imaging Diagnosis

2022 ◽  
Vol 2022 ◽  
pp. 1-12
Author(s):  
Wenfa Jiang ◽  
Ganhua Zeng ◽  
Shuo Wang ◽  
Xiaofeng Wu ◽  
Chenyang Xu
Keyword(s):  
Lung Cancer ◽  
Computer Vision ◽  
Deep Learning ◽  
Malignant Tumors ◽  
Lung Nodule ◽  
Ct Images ◽  
Lung Nodules ◽  
X Rays ◽  
Imaging Diagnosis ◽  
Risk Of Cancer

Lung cancer is one of the malignant tumors with the highest fatality rate and nearest to our lives. It poses a great threat to human health and it mainly occurs in smokers. In our country, with the acceleration of industrialization, environmental pollution, and population aging, the cancer burden of lung cancer is increasing day by day. In the diagnosis of lung cancer, Computed Tomography (CT) images are a fairly common visualization tool. CT images visualize all tissues based on the absorption of X-rays. The diseased parts of the lung are collectively referred to as pulmonary nodules, the shape of nodules is different, and the risk of cancer will vary with the shape of nodules. Computer-aided diagnosis (CAD) is a very suitable method to solve this problem because the computer vision model can quickly scan every part of the CT image of the same quality for analysis and will not be affected by fatigue and emotion. The latest advances in deep learning enable computer vision models to help doctors diagnose various diseases, and in some cases, models have shown greater competitiveness than doctors. Based on the opportunity of technological development, the application of computer vision in medical imaging diagnosis of diseases has important research significance and value. In this paper, we have used a deep learning-based model on CT images of lung cancer and verified its effectiveness in the timely and accurate prediction of lungs disease. The proposed model has three parts: (i) detection of lung nodules, (ii) False Positive Reduction of the detected nodules to filter out “false nodules,” and (iii) classification of benign and malignant lung nodules. Furthermore, different network structures and loss functions were designed and realized at different stages. Additionally, to fine-tune the proposed deep learning-based mode and improve its accuracy in the detection Lung Nodule Detection, Noudule-Net, which is a detection network structure that combines U-Net and RPN, is proposed. Experimental observations have verified that the proposed scheme has exceptionally improved the expected accuracy and precision ratio of the underlined disease.

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