scholarly journals ResNet based Lung Nodules Detection from Computed Tomography Images

2020 ◽  
Vol 9 (4) ◽  
pp. 1711-1714
Keyword(s):  
Neural Network ◽  
Survival Rate ◽  
Early Stage ◽  
Lung Nodule ◽  
Lung Nodules ◽  
Morphological Operations ◽  
Detection Systems ◽  
Lung Nodule Detection ◽  
Computed Tomography Images ◽  
Nodule Detection

Lung cancer have become one of the major threat to human kind over few years. The survival rate of the patient depends mainly on the stage of cancer when it was detected with early stage detection increases survival rate significantly. Many computer aided detection systems were proposed to assist radiologist in detecting lung nodules efficiently. After the success of deep learning neural network in object classification problem, researchers started adopting it for different tasks in medical image processing and hence in lung nodule detection systems. Hence, a lung nodule detection method using ResNet in CT images is proposed. The proposed method consists of two stages, the pre-processing stage and nodule detection stage. The proposed technique uses morphological operations for segmentation of lungs and convolutional neural network for detection of lung nodules. This method is developed with an aim to provide second opinion to radiologists and reduce their workload. LIDC (Lung Image Database Consortium) dataset which contains 1010 CT patients images of chest regions are taken for experimentation. The model was able to achieve top-5 accuracy of 95.24% on test dataset.

scholarly journals A Review of Image Segmentation Methods for Lung Nodule Detection Based on Computed Tomography Images

2018 ◽  
Vol 232 ◽  
pp. 02001 ◽  
Author(s):  
Li Zheng ◽  
Yiran Lei
Keyword(s):  
Differential Operator ◽  
Active Contour ◽  
Lung Nodule ◽  
Region Growing ◽  
Lung Nodules ◽  
Lung Nodule Detection ◽  
Computed Tomography Images ◽  
Nodule Detection ◽  
Significant Step ◽  
Low Sensitivity

The detection and segmentation of lung nodules based on computer tomography images (CT) is a basic and significant step to achieve the robotic needle biopsy. In this paper, we reviewed some typical segmentation algorithms, including thresholding, active contour, differential operator, region growing and watershed. To analyse their performance on lung nodule detection, we applied them to four CT images of different kinds of lung nodules. The results show that thresholding, active contour and differential operator do well in the segmentation of solitary nodules, while region growing has an advantage over the others on segmenting nodules adhere to vessels. For segmentation of semi-transparent nodules, differential operator is an especially suitable choice. Watershed can segment nodules adhere to vessels and semi-transparent nodules well, but it has low sensitivity in solitary nodules.

Multi-level 3D Densenets for False-positive Reduction in Lung Nodule Detection Based on Chest Computed Tomography

2020 ◽  
Vol 16 (8) ◽  
pp. 1004-1021
Author(s):  
Xiaoqi Lu ◽  
Yu Gu ◽  
Lidong Yang ◽  
Baohua Zhang ◽  
Ying Zhao ◽  
...  
Keyword(s):  
False Positive ◽  
Contextual Information ◽  
Lung Nodule ◽  
Positive Sample ◽  
Lung Nodules ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Level Information ◽  
Multi Level ◽  
Cade System

Objective: False-positive nodule reduction is a crucial part of a computer-aided detection (CADe) system, which assists radiologists in accurate lung nodule detection. In this research, a novel scheme using multi-level 3D DenseNet framework is proposed to implement false-positive nodule reduction task. Methods: Multi-level 3D DenseNet models were extended to differentiate lung nodules from falsepositive nodules. First, different models were fed with 3D cubes with different sizes for encoding multi-level contextual information to meet the challenges of the large variations of lung nodules. In addition, image rotation and flipping were utilized to upsample positive samples which consisted of a positive sample set. Furthermore, the 3D DenseNets were designed to keep low-level information of nodules, as densely connected structures in DenseNet can reuse features of lung nodules and then boost feature propagation. Finally, the optimal weighted linear combination of all model scores obtained the best classification result in this research. Results: The proposed method was evaluated with LUNA16 dataset which contained 888 thin-slice CT scans. The performance was validated via 10-fold cross-validation. Both the Free-response Receiver Operating Characteristic (FROC) curve and the Competition Performance Metric (CPM) score show that the proposed scheme can achieve a satisfactory detection performance in the falsepositive reduction track of the LUNA16 challenge. Conclusion: The result shows that the proposed scheme can be significant for false-positive nodule reduction task.

Overview of Computer Aided Detection and Computer Aided Diagnosis Systems for Lung Nodule Detection in Computed Tomography

2020 ◽  
Vol 16 (1) ◽  
pp. 16-26 ◽  
Author(s):  
Shabana Rasheed Ziyad ◽  
Venkatachalam Radha ◽  
Thavavel Vayyapuri
Keyword(s):  
Computed Tomography ◽  
Review Paper ◽  
Lung Nodule ◽  
Lung Nodules ◽  
Computer Aided Detection ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Computer Aided ◽  
Aided Diagnosis

Background: Lung cancer has become a major cause of cancer-related deaths. Detection of potentially malignant lung nodules is essential for the early diagnosis and clinical management of lung cancer. In clinical practice, the interpretation of Computed Tomography (CT) images is challenging for radiologists due to a large number of cases. There is a high rate of false positives in the manual findings. Computer aided detection system (CAD) and computer aided diagnosis systems (CADx) enhance the radiologists in accurately delineating the lung nodules. Objectives: The objective is to analyze CAD and CADx systems for lung nodule detection. It is necessary to review the various techniques followed in CAD and CADx systems proposed and implemented by various research persons. This study aims at analyzing the recent application of various concepts in computer science to each stage of CAD and CADx. Methods: This review paper is special in its own kind because it analyses the various techniques proposed by different eminent researchers in noise removal, contrast enhancement, thorax removal, lung segmentation, bone suppression, segmentation of trachea, classification of nodule and nonnodule and final classification of benign and malignant nodules. Results: A comparison of the performance of different techniques implemented by various researchers for the classification of nodule and non-nodule has been tabulated in the paper. Conclusion: The findings of this review paper will definitely prove to be useful to the research community working on automation of lung nodule detection.

scholarly journals A robust convolutional neural network for lung nodule detection in the presence of foreign bodies

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Manuel Schultheiss ◽  
Sebastian A. Schober ◽  
Marie Lodde ◽  
Jannis Bodden ◽  
Juliane Aichele ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Foreign Bodies ◽  
Lung Nodule ◽  
Lung Nodule Detection ◽  
Nodule Detection

scholarly journals Lung Nodule Detection using Segmentation Approach for Computed Tomography Scan Images

2021 ◽  
Vol 9 (9) ◽  
pp. 1778-1790
Author(s):  
Henil Satra
Keyword(s):  
Computed Tomography ◽  
Image Processing ◽  
Ct Scan ◽  
Lung Nodule ◽  
Morphological Operations ◽  
Lung Nodule Detection ◽  
Contour Segmentation ◽  
Nodule Detection ◽  
Segmentation Approach ◽  
Computed Tomography Image

Abstract: Lung disorders have become really common in today’s world due to growing amount of air pollution, our increased exposure to harmful radiations and our unhealthy lifestyles. Hence, the diagnosis of lung disorders has become of paramount importance. The commonly used Thresholding approaches and morphological operations often fail to detect the peripheral pathology bearing areas. Hence, we present the segmentation approach of the lung tissue for computer aided diagnosis system. We use a novel technique for segmentation of lungs from CT scan (Computed Tomography) of the chest or upper torso. The accuracy of analysis and its implication majorly depends on the kind of segmentation technique used. Hence, it is important that the method used is highly reliable and is successful in nodule detection and classification. We use MATLAB and OpenCV libraries to apply segmentation on CT scan images to get the desired output. We have also created a working proprietary user interface called “PULMONIS” for the ease of doctors and patients to upload the CT scan images and get the output after the image processing is done in the backend. Keywords: Lung nodule detection, Image Processing, Computed Tomography, Image Segmentation, Lung Cancer, Contour Segmentation, MATLAB, OpenCV, Computer Vision.

scholarly journals Research on Lung Nodule Detection Based on Improved Target Detection Network

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Ye Li ◽  
Qian Wu ◽  
Hongwei Sun ◽  
Xuewei Wang
Keyword(s):  
Lung Cancer ◽  
Target Detection ◽  
Block Structure ◽  
Lung Nodule ◽  
Lung Nodules ◽  
Early Symptom ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Public Datasets ◽  
Convolution Method

Lung nodules are an early symptom of lung cancer. The earlier they are found, the more beneficial it is for treatment. However, in practice, Chinese doctors are likely to cause misdiagnosis. Therefore, deep learning is introduced, an improved target detection network is used, and public datasets are used to diagnose and identify lung nodules. This paper selects the Mask-RCNN network and uses the dense block structure of Densenet and the channel shuffle convolution method to improve the Mask-RCNN network. The experimental results prove that proposed algorithm is extremely effective.

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