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

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.

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

10.5753/sbcas.2018.3681 ◽

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.

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Application of Deep Learning in Lung Cancer Imaging Diagnosis

Journal of Healthcare Engineering ◽

10.1155/2022/6107940 ◽

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.

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

10.5753/sbcas.2016.9894 ◽

2020 ◽

Cited By ~ 2

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%.

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Intelligent classification of lung malignancies using deep learning techniques

International Journal of Intelligent Computing and Cybernetics ◽

10.1108/ijicc-07-2021-0147 ◽

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.

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

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-212189 ◽

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.

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

OncoTargets and Therapy ◽

10.2147/ott.s80733 ◽

2015 ◽

pp. 2015 ◽

Cited By ~ 30

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

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Lung cancer diagnosis and staging using firefly algorithm fuzzy C-means segmentation and support vector machine classification of lung nodules

International Journal of Biomedical Engineering and Technology ◽

10.1504/ijbet.2021.119504 ◽

2021 ◽

Vol 37 (2) ◽

pp. 185

Author(s):

M. Lavanya ◽

P. Muthu Kannan ◽

M. Arivalagan

Keyword(s):

Lung Cancer ◽

Support Vector Machine ◽

Cancer Diagnosis ◽

Firefly Algorithm ◽

Support Vector ◽

Lung Nodules ◽

Fuzzy C Means ◽

Support Vector Machine Classification ◽

Lung Cancer Diagnosis

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Deep Learning Based Depthwise Separable Model For Effective Diagnosis And Classification of Lung Ct Images

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.a1439.109119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 1808-1819 ◽

Cited By ~ 1

Keyword(s):

Lung Cancer ◽

Feature Extraction ◽

Deep Learning ◽

Noise Removal ◽

Ct Images ◽

Classification Model ◽

Diagnosis And Classification ◽

Effective Diagnosis ◽

Lung Ct

Lung cancer is a serious illness which leads to increased mortality rate globally. The identification of lung cancer at the beginning stage is the probable method of improving the survival rate of the patients. Generally, Computed Tomography (CT) scan is applied for finding the location of the tumor and determines the stage of cancer. Existing works has presented an effective diagnosis classification model for CT lung images. This paper designs an effective diagnosis and classification model for CT lung images. The presented model involves different stages namely pre-processing, segmentation, feature extraction and classification. The initial stage includes an adaptive histogram based equalization (AHE) model for image enhancement and bilateral filtering (BF) model for noise removal. The pre-processed images are fed into the second stage of watershed segmentation model for effectively segment the images. Then, a deep learning based Xception model is applied for prominent feature extraction and the classification takes place by the use of logistic regression (LR) classifier. A comprehensive simulation is carried out to ensure the effective classification of the lung CT images using a benchmark dataset. The outcome implied the outstanding performance of the presented model on the applied test images.

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