scholarly journals Deep Learning Methods for Lung Cancer Nodule Classification: A Survey

2021 ◽  
Author(s):  
Pavan Kumar Illa ◽  
T. Senthil Kumar ◽  
F. Syed Anwar Hussainy
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Performance Measures ◽  
Research Direction ◽  
Performance Comparison ◽  
Lung Nodules ◽  
X Ray ◽  
Step Detection ◽  
Learning Techniques ◽  
Nodule Classification

Lung cancer is one of the leading causes of cancer related deaths. It is due to the complexity of early detection of nodules. In clinical practice, radiologists find it difficult to determine whether a condition is normal or abnormal by manually analysing CT scan or X-ray images for nodule identification. Currently, various deep learning techniques have been developed to identify lung nodules as benign or malignant, but each technique has its own advantages and drawbacks. This work presents a thorough analysis based on segmentation techniques, Related features-based detection, multi-step detection, automatic detection, and deep convolutional neural network techniques. Performance comparison was conducted on a selected works based on performance measures. A potential research direction for the recognition of lung nodules is given at the end of this study.

scholarly journals Deep Mining Generation of Lung Cancer Malignancy Models from Chest X-ray Images

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6655
Author(s):  
Michael Horry ◽  
Subrata Chakraborty ◽  
Biswajeet Pradhan ◽  
Manoranjan Paul ◽  
Douglas Gomes ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Computer Vision ◽  
Deep Learning ◽  
Decision Tree ◽  
Decision Trees ◽  
Lung Nodules ◽  
X Rays ◽  
Learning Models ◽  
X Ray ◽  
Chest X Ray

Lung cancer is the leading cause of cancer death and morbidity worldwide. Many studies have shown machine learning models to be effective in detecting lung nodules from chest X-ray images. However, these techniques have yet to be embraced by the medical community due to several practical, ethical, and regulatory constraints stemming from the “black-box” nature of deep learning models. Additionally, most lung nodules visible on chest X-rays are benign; therefore, the narrow task of computer vision-based lung nodule detection cannot be equated to automated lung cancer detection. Addressing both concerns, this study introduces a novel hybrid deep learning and decision tree-based computer vision model, which presents lung cancer malignancy predictions as interpretable decision trees. The deep learning component of this process is trained using a large publicly available dataset on pathological biomarkers associated with lung cancer. These models are then used to inference biomarker scores for chest X-ray images from two independent data sets, for which malignancy metadata is available. Next, multi-variate predictive models were mined by fitting shallow decision trees to the malignancy stratified datasets and interrogating a range of metrics to determine the best model. The best decision tree model achieved sensitivity and specificity of 86.7% and 80.0%, respectively, with a positive predictive value of 92.9%. Decision trees mined using this method may be considered as a starting point for refinement into clinically useful multi-variate lung cancer malignancy models for implementation as a workflow augmentation tool to improve the efficiency of human radiologists.

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.

COVID-19 Detection: An Approach Using X-Ray Images and Deep Learning Techniques

2021 ◽  
pp. 7-16
Author(s):  
Akshay Raina ◽  
Shubham Mahajan ◽  
Ch. Vanipriya ◽  
Anil Bhardwaj ◽  
Amit Kant Pandit
Keyword(s):  
Deep Learning ◽  
X Ray ◽  
Learning Techniques

Efficient Large-Scale Stance Detection in Tweets

2020 ◽  
pp. 667-683
Author(s):  
Yilin Yan ◽  
Jonathan Chen ◽  
Mei-Ling Shyu
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Large Scale ◽  
Research Direction ◽  
Detection Methods ◽  
Use Case ◽  
Learning Techniques ◽  
Test Use ◽  
Presidential Election Campaign ◽  
Important Research Direction

Stance detection is an important research direction which attempts to automatically determine the attitude (positive, negative, or neutral) of the author of text (such as tweets), towards a target. Nowadays, a number of frameworks have been proposed using deep learning techniques that show promising results in application domains such as automatic speech recognition and computer vision, as well as natural language processing (NLP). This article shows a novel deep learning-based fast stance detection framework in bipolar affinities on Twitter. It is noted that millions of tweets regarding Clinton and Trump were produced per day on Twitter during the 2016 United States presidential election campaign, and thus it is used as a test use case because of its significant and unique counter-factual properties. In addition, stance detection can be utilized to imply the political tendency of the general public. Experimental results show that the proposed framework achieves high accuracy results when compared to several existing stance detection methods.

scholarly journals A deep-learning technique for phase identification in multiphase inorganic compounds using synthetic XRD powder patterns

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Jin-Woong Lee ◽  
Woon Bae Park ◽  
Jin Hee Lee ◽  
Satendra Pal Singh ◽  
Kee-Sun Sohn
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Inorganic Compounds ◽  
Phase Identification ◽  
X Ray Diffraction ◽  
X Ray ◽  
Learning Techniques ◽  
Learning Technique ◽  
Xrd Patterns

AbstractHere we report a facile, prompt protocol based on deep-learning techniques to sort out intricate phase identification and quantification problems in complex multiphase inorganic compounds. We simulate plausible powder X-ray diffraction (XRD) patterns for 170 inorganic compounds in the Sr-Li-Al-O quaternary compositional pool, wherein promising LED phosphors have been recently discovered. Finally, 1,785,405 synthetic XRD patterns are prepared by combinatorically mixing the simulated powder XRD patterns of 170 inorganic compounds. Convolutional neural network (CNN) models are built and eventually trained using this large prepared dataset. The fully trained CNN model promptly and accurately identifies the constituent phases in complex multiphase inorganic compounds. Although the CNN is trained using the simulated XRD data, a test with real experimental XRD data returns an accuracy of nearly 100% for phase identification and 86% for three-step-phase-fraction quantification.

scholarly journals Early Lung Cancer Detection using Deep Learning Optimization

2020 ◽  
Vol 16 (06) ◽  
pp. 82
Author(s):  
Ahmed Elnakib ◽  
Hanan M. Amer ◽  
Fatma E.Z. Abou-Chadi
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Early Detection ◽  
Low Dose ◽  
Lung Nodules ◽  
Svm Classifier ◽  
Detection Accuracy ◽  
Early Lung Cancer ◽  
Action Project ◽  
Cade System

This paper proposes a Computer Aided Detection (CADe) system for early detection of lung nodules from low dose computed tomography (LDCT) images. The proposed system initially pre-process the raw data to improve the contrast of the low dose images. Compact deep learning features are then extracted by investigating different deep learning architectures, including Alex, VGG16, and VGG19 networks. To optimize the extracted set of features, a genetic algorithm (GA) is trained to select the most relevant features for early detection. Finally, different types of classifiers are tested in order to accurately detect the lung nodules. The system is tested on 320 LDCT images from 50 different subjects, using an online public lung database, i.e., the International Early Lung Cancer Action Project, I-ELCAP. The proposed system, using VGG19 architecture and SVM classifier, achieves the best detection accuracy of 96.25%, sensitivity of 97.5%, and specificity of 95%. Compared to other state-of-the-art methods, the proposed system shows a promising results.

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