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.

Lung Nodule Detectability of Artificial Intelligence-assisted CT Image Reading in Lung Cancer Screening

2021 ◽  
Vol 17 ◽  
Author(s):  
Yaping Zhang ◽  
Beibei Jiang ◽  
Lu Zhang ◽  
Marcel J.W. Greuter ◽  
Geertruida H. de Bock ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Detection Rate ◽  
Lung Nodule ◽  
Chest Ct ◽  
Lung Nodules ◽  
Lung Nodule Detection ◽  
Radiology Reports ◽  
Nodule Detection ◽  
Assisted Reading

Background: Artificial intelligence (AI)-based automatic lung nodule detection system improves the detection rate of nodules. It is important to evaluate the clinical value of AI system by comparing AI-assisted nodule detection with actu-al radiology reports. Objective: To compare the detection rate of lung nodules between the actual radiology reports and AI-assisted reading in lung cancer CT screening. Methods: Participants in chest CT screening from November to December 2019 were retrospectively included. In the real-world radiologist observation, 14 residents and 15 radiologists participated to finalize radiology reports. In AI-assisted reading, one resident and one radiologist reevaluated all subjects with the assistance of an AI system to lo-cate and measure the detected lung nodules. A reading panel determined the type and number of detected lung nodules between these two methods. Results: In 860 participants (57±7 years), the reading panel confirmed 250 patients with >1 solid nodule, while radiolo-gists observed 131, lower than 247 by AI-assisted reading (p<0.001). The panel confirmed 111 patients with >1 non-solid nodule, whereas radiologist observation identified 28, lower than 110 by AI-assisted reading (p<0.001). The accuracy and sensitivity of radiologist observation for solid nodules were 86.2% and 52.4%, lower than 99.1% and 98.8% by AI-assisted reading, respectively. These metrics were 90.4% and 25.2% for non-solid nodules, lower than 98.8% and 99.1% by AI-assisted reading, respectively. Conclusion: Comparing with the actual radiology reports, AI-assisted reading greatly improves the accuracy and sensi-tivity of nodule detection in chest CT, which benefits lung nodule detection, especially for non-solid nodules.

scholarly journals Ant Colony Optimization Approaches to Clustering of Lung Nodules from CT Images

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Ravichandran C. Gopalakrishnan ◽  
Veerakumar Kuppusamy
Keyword(s):  
Lung Cancer ◽  
Genetic Algorithm ◽  
Lung Nodule ◽  
Machine Learning Algorithms ◽  
Lung Nodules ◽  
Watershed Algorithm ◽  
Lung Nodule Detection ◽  
Novel Approach ◽  
Nodule Detection ◽  
Shape And Size

Lung cancer is becoming a threat to mankind. Applying machine learning algorithms for detection and segmentation of irregular shaped lung nodules remains a remarkable milestone in CT scan image analysis research. In this paper, we apply ACO algorithm for lung nodule detection. We have compared the performance against three other algorithms, namely, Otsu algorithm, watershed algorithm, and global region based segmentation. In addition, we suggest a novel approach which involves variations of ACO, namely, refined ACO, logical ACO, and variant ACO. Variant ACO shows better reduction in false positives. In addition we propose black circular neighborhood approach to detect nodule centers from the edge detected image. Genetic algorithm based clustering is performed to cluster the nodules based on intensity, shape, and size. The performance of the overall approach is compared with hierarchical clustering to establish the improvisation in the proposed approach.

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 Evaluation of the Effectiveness of Artificial Intelligence Chest CT Lung Nodule Detection Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Fukui Liang ◽  
Caiqin Li ◽  
Xiaoqin Fu
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Deep Learning ◽  
Medical Imaging ◽  
Lung Nodule ◽  
Pulmonary Nodules ◽  
Chest Ct ◽  
Detection Sensitivity ◽  
Lung Nodule Detection ◽  
Nodule Detection

Lung cancer is one of the most malignant tumors. If it can be detected early and treated actively, it can effectively improve a patient’s survival rate. Therefore, early diagnosis of lung cancer is very important. Early-stage lung cancer usually appears as a solitary lung nodule on medical imaging. It usually appears as a round or nearly round dense shadow in the chest radiograph. It is difficult to distinguish lung nodules and lung soft tissues with the naked eye. Therefore, this article proposes a deep learning-based artificial intelligence chest CT lung nodule detection performance evaluation study, aiming to evaluate the value of chest CT imaging technology in the detection of noncalcified nodules and provide help for the detection and treatment of lung cancer. In this article, the Lung Medical Imaging Database Consortium (LIDC) was selected to obtain 536 usable cases based on inclusion criteria; 80 cases were selected for examination, artificial intelligence software, radiologists, and thoracic imaging specialists. Using 80 pulmonary nodules detection in each case, the pathological type of pulmonary nodules, nonlime tuberculous test results, detection sensitivity, false negative rate, false positive rate, and CT findings were individually analyzed, and the detection efficiency software of artificial intelligence was evaluated. Experiments have proved that the sensitivity of artificial intelligence software to detect noncalcified nodules in the pleural, peripheral, central, and hilar areas is higher than that of radiologists, indicating that the method proposed in this article has achieved good detection results. It has a better nodule detection sensitivity than a radiologist, reducing the complexity of the detection process.

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.

scholarly journals Lung Cancer Classification Using Squeeze and Excitation Convolutional Neural Networks with Grad Cam++ Class Activation Function

2021 ◽  
Vol 38 (4) ◽  
pp. 1103-1112
Author(s):  
Eali Stephen Neal Joshua ◽  
Debnath Bhattacharyya ◽  
Midhun Chakkravarthy ◽  
Hye-Jin Kim
Keyword(s):  
Lung Cancer ◽  
Patient Survival ◽  
Lung Nodule ◽  
Activation Function ◽  
Cancer Classification ◽  
Systematic Evaluation ◽  
Lung Nodules ◽  
Nodule Detection ◽  
Lung Cancer Therapy ◽  
Nodule Classification

The leading cause of cancer-related death globally has been identified as lung cancer. Early lung nodule detection is critical for lung cancer therapy and patient survival. The Gard Cam++ Class Activation Function is used with a squeeze-and-excite network to provide a revolutionary method for differentiating malignant from benign lung nodules on CT scans. The new SENET (Squeeze-and-Excitation Networks) Grad Cam++ module, which combines the features calibration and discrimination benefits of SENET, has been shown to have a substantial potential for improving feature discriminability in lung cancer classification. According to the publicly available LUng Nodule Analysis 2016 (LUNA16) database, when assessed on 1230 nodules, the technique achieved an AUC of 0.9664 and an accuracy of 97.08% (600 malignant and 630 benign). The favorable results demonstrate the robustness of our technique to nodule classification, which we anticipate will be valuable in the future. The technology's objective is to aid radiologists in evaluating diagnostic data and differentiating benign from malignant lung nodules on CT images. To our knowledge, no systematic evaluation of SENET usefulness in classifying lung nodules has been done.

Sign in / Sign up

Export Citation Format

Share Document