scholarly journals Computer-aided classification of lung nodules on computed tomography images via deep learning technique

2015 ◽  
pp. 2015 ◽  
Author(s):  
Yu-Jen Yu-Jen Chen ◽  
Kai-Lung Hua ◽  
Che-Hao Hsu ◽  
Wen-Huang Cheng ◽  
Shintami Chusnul Hidayati
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Lung Nodules ◽  
Computed Tomography Images ◽  
Computer Aided ◽  
Learning Technique

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 Development of Computer-aided Diagnostic System for Detection of Lung Nodules in Three-dimensional Computed Tomography Images

2006 ◽  
Vol 62 (4) ◽  
pp. 555-564 ◽  
Author(s):  
Megumi Yamamoto ◽  
Takayuki Ishida ◽  
Ikuo Kawashita ◽  
Masayuki Kagemoto ◽  
Koichi Fujikawa ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Three Dimensional ◽  
Diagnostic System ◽  
Lung Nodules ◽  
Computed Tomography Images ◽  
Computer Aided

scholarly journals Classification of malignancy of lung nodules in CT images using Convolutional Neural Network

2020 ◽  
Author(s):  
Giovanni Da Silva ◽  
Aristófanes Silva ◽  
Anselmo De Paiva ◽  
Marcelo Gattass
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Survival Rates ◽  
Texture Features ◽  
Image Database ◽  
Lung Nodules ◽  
Learning Technique

Lung cancer presents the highest mortality rate, besides being one of the smallest survival rates after diagnosis. Thereby, early detection is extremely important for the diagnosis and treatment. This paper proposes three different architectures of Convolutional Neural Network (CNN), which is a deep learning technique, for classification of malignancy of lung nodules without computing the morphology and texture features. The methodology was tested onto the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), with the best accuracy of 82.3%, sensitivity of 79.4% and specificity 83.8%.

scholarly journals Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
QingZeng Song ◽  
Lei Zhao ◽  
XingKe Luo ◽  
XueChen Dou
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Lung Nodules ◽  
Ct Image ◽  
Powerful Method ◽  
Imaging Diagnosis ◽  
Computed Tomography Images ◽  
Common Cancer

Lung cancer is the most common cancer that cannot be ignored and cause death with late health care. Currently, CT can be used to help doctors detect the lung cancer in the early stages. In many cases, the diagnosis of identifying the lung cancer depends on the experience of doctors, which may ignore some patients and cause some problems. Deep learning has been proved as a popular and powerful method in many medical imaging diagnosis areas. In this paper, three types of deep neural networks (e.g., CNN, DNN, and SAE) are designed for lung cancer calcification. Those networks are applied to the CT image classification task with some modification for the benign and malignant lung nodules. Those networks were evaluated on the LIDC-IDRI database. The experimental results show that the CNN network archived the best performance with an accuracy of 84.15%, sensitivity of 83.96%, and specificity of 84.32%, which has the best result among the three networks.

scholarly journals Using Deep Learning for Classification of Early Lung Nodules on Computed Tomography Images

2018 ◽  
Author(s):  
Lucas Lima ◽  
Marcelo Oliveira
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Pulmonary Nodules ◽  
Lung Nodules ◽  
Lung Cancer Patients ◽  
Early Stages ◽  
Computed Tomography Images ◽  
Area Under Roc Curve ◽  
Small Pulmonary Nodules

Lung cancer is the leading cause of cancer mortality, accounting for approximately 20% of all cancer-related deaths. Nevertheless, despite the development of new therapeutic agents and technologies, only 16% of lung cancer patients are diagnosed at early stages. Therefore, to diagnose in early stages, when the nodules are very small, is a complex task for specialists and presents some challenges. To assist the specialists, the main purpose of this work is to propose the use of Deep Learning to classify 25,200 small pulmonary nodules balanced with diameter 5-10mm. The result was of 0.992 (+/- 0.001) of area under ROC curve using 10-fold cross validation. The proposed method showed to be promising to assist the specialists in classification of small lung nodules.

scholarly journals Detection and Severity Classification of COVID-19 in CT Images Using Deep Learning

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 893
Author(s):  
Yazan Qiblawey ◽  
Anas Tahir ◽  
Muhammad E. H. Chowdhury ◽  
Amith Khandakar ◽  
Serkan Kiranyaz ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Early Stage ◽  
Similarity Coefficient ◽  
Dice Similarity Coefficient ◽  
Lung Region ◽  
Computed Tomography Images ◽  
Feature Pyramid ◽  
Severity Levels

Detecting COVID-19 at an early stage is essential to reduce the mortality risk of the patients. In this study, a cascaded system is proposed to segment the lung, detect, localize, and quantify COVID-19 infections from computed tomography images. An extensive set of experiments were performed using Encoder–Decoder Convolutional Neural Networks (ED-CNNs), UNet, and Feature Pyramid Network (FPN), with different backbone (encoder) structures using the variants of DenseNet and ResNet. The conducted experiments for lung region segmentation showed a Dice Similarity Coefficient (DSC) of 97.19% and Intersection over Union (IoU) of 95.10% using U-Net model with the DenseNet 161 encoder. Furthermore, the proposed system achieved an elegant performance for COVID-19 infection segmentation with a DSC of 94.13% and IoU of 91.85% using the FPN with DenseNet201 encoder. The proposed system can reliably localize infections of various shapes and sizes, especially small infection regions, which are rarely considered in recent studies. Moreover, the proposed system achieved high COVID-19 detection performance with 99.64% sensitivity and 98.72% specificity. Finally, the system was able to discriminate between different severity levels of COVID-19 infection over a dataset of 1110 subjects with sensitivity values of 98.3%, 71.2%, 77.8%, and 100% for mild, moderate, severe, and critical, respectively.

Liver segmentation from computed tomography images using cascade deep learning

2021 ◽  
pp. 105095
Author(s):  
José Denes Lima Araújo ◽  
Luana Batista da Cruz ◽  
João Otávio Bandeira Diniz ◽  
Jonnison Lima Ferreira ◽  
Aristófanes Corrêa Silva ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Liver Segmentation ◽  
Computed Tomography Images
Sign in / Sign up

Export Citation Format

Share Document