A Hybrid deep learning model for effective segmentation and classification of lung nodules from CT images

2021 ◽  
pp. 1-13
Author(s):  
Malathi Murugesan ◽  
Kalaiselvi Kaliannan ◽  
Shankarlal Balraj ◽  
Kokila Singaram ◽  
Thenmalar Kaliannan ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Lung Tumor ◽  
Median Filter ◽  
Lung Nodule ◽  
Rapid Identification ◽  
Lung Nodules ◽  
Classification Models ◽  
Deep Learning Model

Deep learning algorithms will be used to detect lung nodule anomalies at an earlier stage. The primary goal of this effort is to properly identify lung cancer, which is critical in preserving a person’s life. Lung cancer has been a source of concern for people all around the world for decades. Several researchers presented numerous issues and solutions for various stages of a computer-aided system for diagnosing lung cancer in its early stages, as well as information about lung cancer. Computer vision is one of the field of artificial intelligence this is a better way to detect and prevent the lung cancer. This study focuses on the stages involved in detecting lung tumor regions, namely pre-processing, segmentation, and classification models. An adaptive median filter is used in pre-processing to identify the noise. The work’s originality seeks to create a simple yet effective model for the rapid identification and U-net architecture based segmentation of lung nodules. This approach focuses on the identification and segmentation of lung cancer by detecting picture normalcy and abnormalities.

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.

1140P Validation of a deep learning model for the classification of lung cancer in a large cohort of biopsied samples

2021 ◽  
Vol 32 ◽  
pp. S926-S927
Author(s):  
G. Toyokawa ◽  
Y. Yamada ◽  
N. Haratake ◽  
Y. Shiraishi ◽  
T. Takenaka ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Learning Model ◽  
Large Cohort ◽  
Deep Learning Model

scholarly journals Novel Techniques for Classification of Lung Nodules using Deep Learning Approach

2019 ◽  
Vol 13 (1) ◽  
pp. 120-126
Author(s):  
K. Bhavanishankar ◽  
M. V. Sudhamani
Keyword(s):  
Deep Learning ◽  
Lung Nodule ◽  
Ground Truth ◽  
Lung Nodules ◽  
Learning Approach ◽  
Data Sets ◽  
Ground Truth Data ◽  
Series Of Experiments ◽  
Sensitivity Specificity

Objective: Lung cancer is proving to be one of the deadliest diseases that is haunting mankind in recent years. Timely detection of the lung nodules would surely enhance the survival rate. This paper focusses on the classification of candidate lung nodules into nodules/non-nodules in a CT scan of the patient. A deep learning approach –autoencoder is used for the classification. Investigation/Methodology: Candidate lung nodule patches obtained as the results of the lung segmentation are considered as input to the autoencoder model. The ground truth data from the LIDC repository is prepared and is submitted to the autoencoder training module. After a series of experiments, it is decided to use 4-stacked autoencoder. The model is trained for over 600 LIDC cases and the trained module is tested for remaining data sets. Results: The results of the classification are evaluated with respect to performance measures such as sensitivity, specificity, and accuracy. The results obtained are also compared with other related works and the proposed approach was found to be better by 6.2% with respect to accuracy. Conclusion: In this paper, a deep learning approach –autoencoder has been used for the classification of candidate lung nodules into nodules/non-nodules. The performance of the proposed approach was evaluated with respect to sensitivity, specificity, and accuracy and the obtained values are 82.6%, 91.3%, and 87.0%, respectively. This result is then compared with existing related works and an improvement of 6.2% with respect to accuracy has been observed.

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%.

Intelligent classification of lung malignancies using deep learning techniques

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Priyanka Yadlapalli ◽  
D. Bhavana ◽  
Suryanarayana Gunnam
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Ct Scan ◽  
Transfer Learning ◽  
Confusion Matrix ◽  
Chest Ct ◽  
Lung Nodules ◽  
Content Type ◽  
Chest Ct Scan

PurposeComputed tomography (CT) scan can provide valuable information in the diagnosis of lung diseases. To detect the location of the cancerous lung nodules, this work uses novel deep learning methods. The majority of the early investigations used CT, magnetic resonance and mammography imaging. Using appropriate procedures, the professional doctor in this sector analyses these images to discover and diagnose the various degrees of lung cancer. All of the methods used to discover and detect cancer illnesses are time-consuming, expensive and stressful for the patients. To address all of these issues, appropriate deep learning approaches for analyzing these medical images, which included CT scan images, were utilized.Design/methodology/approachRadiologists currently employ chest CT scans to detect lung cancer at an early stage. In certain situations, radiologists' perception plays a critical role in identifying lung melanoma which is incorrectly detected. Deep learning is a new, capable and influential approach for predicting medical images. In this paper, the authors employed deep transfer learning algorithms for intelligent classification of lung nodules. Convolutional neural networks (VGG16, VGG19, MobileNet and DenseNet169) are used to constrain the input and output layers of a chest CT scan image dataset.FindingsThe collection includes normal chest CT scan pictures as well as images from two kinds of lung cancer, squamous and adenocarcinoma impacted chest CT scan images. According to the confusion matrix results, the VGG16 transfer learning technique has the highest accuracy in lung cancer classification with 91.28% accuracy, followed by VGG19 with 89.39%, MobileNet with 85.60% and DenseNet169 with 83.71% accuracy, which is analyzed using Google Collaborator.Originality/valueThe proposed approach using VGG16 maximizes the classification accuracy when compared to VGG19, MobileNet and DenseNet169. The results are validated by computing the confusion matrix for each network type.

scholarly journals A deep learning model for the classification of indeterminate lung carcinoma in biopsy whole slide images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Fahdi Kanavati ◽  
Gouji Toyokawa ◽  
Seiya Momosaki ◽  
Hiroaki Takeoka ◽  
Masaki Okamoto ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Area Under The Curve ◽  
Learning Model ◽  
Primary Sources ◽  
Independent Test ◽  
Hematoxylin And Eosin ◽  
Deep Learning Model ◽  
Whole Slide Images

AbstractThe differentiation between major histological types of lung cancer, such as adenocarcinoma (ADC), squamous cell carcinoma (SCC), and small-cell lung cancer (SCLC) is of crucial importance for determining optimum cancer treatment. Hematoxylin and Eosin (H&E)-stained slides of small transbronchial lung biopsy (TBLB) are one of the primary sources for making a diagnosis; however, a subset of cases present a challenge for pathologists to diagnose from H&E-stained slides alone, and these either require further immunohistochemistry or are deferred to surgical resection for definitive diagnosis. We trained a deep learning model to classify H&E-stained Whole Slide Images of TBLB specimens into ADC, SCC, SCLC, and non-neoplastic using a training set of 579 WSIs. The trained model was capable of classifying an independent test set of 83 challenging indeterminate cases with a receiver operator curve area under the curve (AUC) of 0.99. We further evaluated the model on four independent test sets—one TBLB and three surgical, with combined total of 2407 WSIs—demonstrating highly promising results with AUCs ranging from 0.94 to 0.99.

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 Deep learning analysis for automatic lung nodule detection.

2019 ◽  
Vol 5 (suppl) ◽  
pp. 27-27
Author(s):  
Xiaohua Liu
Keyword(s):  
Lung Cancer ◽  
Image Processing ◽  
Deep Learning ◽  
Detection System ◽  
Lung Nodule ◽  
Lung Lesion ◽  
Lung Nodules ◽  
Cad System ◽  
Learning Analysis ◽  
Lung Ct

27 Background: The prevalence of lung cancer has been increased markedly in worldwide range with growing clinical significance, the quantitative and qualitative analysis on lung nodules has proven to be important for the early-detection of lung cancer as well as its treatment in clinical practice. However, lung lesion screening performed by radiologists can be very time-consuming and its accuracy varies depending on doctor’s individual experiences. In this study, we aim to build up a robust CAD system that automatically detects the lesion locations and quantitatively characterizes the detected lesions on CT images. Methods: Specifically, we employed the deep learning analysis for lesion detection in patients and performed image processing techniques to generate quantitative morphology features for assisting lesion diagnosis . The data collected includes 3956 lung CT series (slice thickness≤3mm) with multiple lung nodules from 15 Class-A hospitals in China , 1155 lung CT scan from Luna16 dataset as well as CT scans from Kaggle dataset (Data Science Bowl 2017). Lung nodule annotation was then performed by two experienced radiologists and further assessed by four senior associate chief physicians. The obtained CT images were randomly selected and split to construct training, validation and test dataset. After preprocessing, a pre-trained ResNet18 framework is transferred to develop a robust detection system to detect the possible lung lesion locations with corresponding probabilities. Results: The resulting detection system yields FROC of 0.4663, recall of 82.46%, precision of 36.06% for 5~30mm nodules. Each detected lesion was labeled by its bounding box and was then analyzed through image processing algorithm to generate diagnostic assisting features, including longest diameter, shortest diameter, volume, largest cross section area as well as its density type (calcify, solid, partial solid, and ground-glass opacity). Conclusions: The proposed CAD system offers a fast and convenient approach for assisting the diagnosis of lung nodule pathologies, and it is beneficial to relate our research to the current framework of lung cancer diagnosis.

scholarly journals The Effect of Low Carbohydrate Diets on Preventing and Treating Carcinogen-Induced Lung Cancer in Mice

2020 ◽  
Vol 4 (Supplement_2) ◽  
pp. 323-323
Author(s):  
Ingrid Elisia ◽  
Brandon Cho ◽  
Mariah Hay ◽  
Michelle Yeung ◽  
Sara Kowalski ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Soy Protein ◽  
Lung Tumor ◽  
Lung Nodule ◽  
Lung Tumors ◽  
Western Diet ◽  
Lung Nodules ◽  
Nodule Formation ◽  
Significant Difference ◽  
Low Carbohydrate

Abstract Objectives Since cancer cells typically rely more on glycolysis than normal cells, we hypothesized that lowering carbohydrate intake may reduce cancer risk. We aimed to investigate the efficacy of low-carbohydrate (CHO) diets in preventing and treating a tobacco-specific carcinogen-induced lung cancer in female A/J mice. Methods We evaluated the role of different types of CHO (easily digestible vs resistant), protein (casein vs. soy) and fat (fish vs. coconut vs. a mixture of oils) in modulating 4-(N-methyl-N-nitrosamino)-1-(3- pyridyl)-1-butanone (NNK)-induced lung nodule formation in these mice. To assess the efficacy of these diets in preventing NNK-induced lung nodule formation, we put these mice in the different diets for 2 weeks, intraperitoneally-injected NNK once a week for two weeks to initiate lung nodule formation. After 5 months, the lung nodules in these mice were counted. Results The lowering of easily digestible CHO significantly reduced constitutive blood glucose levels and lung nodule formation in the mice. Interestingly, diets low in easily digestible starch, high in fish oil (FO) and soy protein (15%Amylose/Soy/FO) were the most effective at preventing the formation of NNK-induced lung nodules. To determine if this 15%Amylose/Soy/FO is also effective at slowing tumor progression, we fed NNK-injected A/J mice a Western diet until tumors were established (5 months post NNK) and then either switched them to the 15%Amylose/Soy/FO or kept them on the Western diet for 5 additional months. The 15%Amylose/Soy/FO diet prevented the formation of additional lung tumor nodules and reduced the size of the tumors, although no significant difference was observed in tumor stage.  The reduction in size of the lung tumors on the 15%Amylose/Soy/FO diet was not due to a lower tumor proliferation (Ki67 index) but an increase in apoptosis, as determined by TUNEL assays. Conclusions We conclude that a diet change that lowers glucose intake, incorporates FO and soy protein may be effective not only in preventing lung cancer formation but also in slowing the growth of established lung tumors. Funding Sources Lotte & John Hecht Memorial Foundation.

Sign in / Sign up

Export Citation Format

Share Document