Fuzzy relevance vector machine based classification of lung nodules in computed tomography images

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.

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- Validation of a New Image-Based Approach for the Accurate Estimating of the Growth Rate of Detected Lung Nodules Using Real Computed Tomography Images and Elastic Phantoms Generated by State-of-the-Art Microfluidics Technology

Lung Imaging and Computer Aided Diagnosis ◽

10.1201/b11106-21 ◽

2016 ◽

pp. 418-433

Keyword(s):

Computed Tomography ◽

Growth Rate ◽

State Of The Art ◽

Lung Nodules ◽

Computed Tomography Images

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Diagnosis of COVID-19 Infection Using Three-Dimensional Semantic Segmentation and Classification of Computed Tomography Images

Computers Materials & Continua ◽

10.32604/cmc.2021.014199 ◽

2021 ◽

Vol 68 (2) ◽

pp. 2451-2467

Author(s):

Javaria Amin ◽

Muhammad Sharif ◽

Muhammad Almas Anjum ◽

Yunyoung Nam ◽

Seifedine Kadry ◽

...

Keyword(s):

Computed Tomography ◽

Three Dimensional ◽

Semantic Segmentation ◽

Computed Tomography Images

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Accurate classification of nodules and non‐nodules from computed tomography images based on radiomics and machine learning algorithms

International Journal of Imaging Systems and Technology ◽

10.1002/ima.22675 ◽

2021 ◽

Author(s):

Xiaofang Zhang ◽

Bin Zhang ◽

Xiaomin Liu ◽

Jie Dong ◽

Shujun Zhao ◽

...

Keyword(s):

Machine Learning ◽

Computed Tomography ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Computed Tomography Images

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New classification of dynamic computed tomography images predictive of malignant characteristics of hepatocellular carcinoma

Hepatology Research ◽

10.1111/j.1872-034x.2010.00703.x ◽

2010 ◽

Vol 40 (10) ◽

pp. 1006-1014 ◽

Cited By ~ 32

Author(s):

Yusuke Kawamura ◽

Kenji Ikeda ◽

Miharu Hirakawa ◽

Hiromi Yatsuji ◽

Hitomi Sezaki ◽

...

Keyword(s):

Computed Tomography ◽

Hepatocellular Carcinoma ◽

Dynamic Computed Tomography ◽

New Classification ◽

Computed Tomography Images

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Wavelet statistical texture features-based segmentation and classification of brain computed tomography images

IET Image Processing ◽

10.1049/iet-ipr.2012.0073 ◽

2013 ◽

Vol 7 (1) ◽

pp. 25-32 ◽

Cited By ~ 32

Author(s):

A. Padma Nanthagopal ◽

R. Sukanesh

Keyword(s):

Computed Tomography ◽

Texture Features ◽

Brain Computed Tomography ◽

Computed Tomography Images

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Overview of Computer Aided Detection and Computer Aided Diagnosis Systems for Lung Nodule Detection in Computed Tomography

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405615666190206153321 ◽

2020 ◽

Vol 16 (1) ◽

pp. 16-26 ◽

Cited By ~ 1

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.

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Classification of Computed Tomography Images in Different Slice Positions Using Deep Learning

Journal of Healthcare Engineering ◽

10.1155/2018/1753480 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 9

Author(s):

Hiroyuki Sugimori

Keyword(s):

Computed Tomography ◽

Ct Imaging ◽

Classification Models ◽

Training Models ◽

Computed Tomography Images ◽

Contrast Enhanced ◽

Noncontrast Ct ◽

Time Required ◽

The Relationship

This study aimed at elucidating the relationship between the number of computed tomography (CT) images, including data concerning the accuracy of models and contrast enhancement for classifying the images. We enrolled 1539 patients who underwent contrast or noncontrast CT imaging, followed by dividing the CT imaging dataset for creating classification models into 10 classes for brain, neck, chest, abdomen, and pelvis with contrast-enhanced and plain imaging. The number of images prepared in each class were 100, 500, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, and 10,000. Accordingly, the names of datasets were defined as 0.1K, 0.5K, 1K, 2K, 3K, 4K, 5K, 6K, 7K, 8K, 9K, and 10K, respectively. We subsequently created and evaluated the models and compared the convolutional neural network (CNN) architecture between AlexNet and GoogLeNet. The time required for training models of AlexNet was lesser than that for GoogLeNet. The best overall accuracy for the classification of 10 classes was 0.721 with the 10K dataset of GoogLeNet. Furthermore, the best overall accuracy for the classification of the slice position without contrast media was 0.862 with the 2K dataset of AlexNet.

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