Computer-aided classification of lung nodules on CT images with expert knowledge

2021 ◽  
Author(s):  
Chuangye Wan ◽  
Ling Ma ◽  
Xiabi Liu ◽  
Baowei Fei
Keyword(s):  
Expert Knowledge ◽  
Ct Images ◽  
Lung Nodules ◽  
Computer Aided

scholarly journals Shape-Based Computer-Aided Detection of Lung Nodules in Thoracic CT Images

2009 ◽  
Vol 56 (7) ◽  
pp. 1810-1820 ◽  
Author(s):  
Xujiong Ye ◽  
Xinyu Lin ◽  
J. Dehmeshki ◽  
G. Slabaugh ◽  
G. Beddoe
Keyword(s):  
Ct Images ◽  
Lung Nodules ◽  
Computer Aided Detection ◽  
Thoracic Ct ◽  
Computer Aided

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 Computer Aided Diagnosis System using Watershed Segmentation with Xception Based Classification Model for Lung CT Images

2019 ◽  
Vol 9 (1) ◽  
pp. 3625-3635
Keyword(s):  
Early Recognition ◽  
Noise Removal ◽  
Ct Images ◽  
Computer Aided Diagnosis ◽  
Classification Model ◽  
Watershed Segmentation ◽  
Computer Aided ◽  
Aided Diagnosis ◽  
Lung Ct

Early recognition and classification of pulmonary nodules by the use of computer-aided diagnosis (CAD) tools finds useful to reduce the death rate due to the illness of lung cancer. This paper devises a new CAD tool utilizing a segmentation based classification process for lung CT images. Initially, the input CT images are pre-processed by image enhancement and noise removal process. Then, watershed segmentation model is employed for the segmentation of the pre-processed images. Subsequently, the feature extraction process is carried out using Xecption model and random forest (RF) classifier is used of the identification of lung CT images as normal, benign or malignant. The use of RF model results to effective classification of the applied images. This model undergoes extensive experimentation against a benchmark lung CT image dataset and the results are investigated under several aspects. The obtained outcome pointed out the significant performance of the presented model over the compared methods.

Classification of benign and malignant lung nodules from CT images based on hybrid features

2019 ◽  
Vol 64 (12) ◽  
pp. 125011 ◽  
Author(s):  
Guobin Zhang ◽  
Zhiyong Yang ◽  
Li Gong ◽  
Shan Jiang ◽  
Lu Wang
Keyword(s):  
Ct Images ◽  
Lung Nodules ◽  
Hybrid Features

Classification of Lung Nodules Using CT Images Based on Texture Features and Fractal Dimension Transformation

2019 ◽  
Vol 99 (2) ◽  
pp. 235-239 ◽  
Author(s):  
V. F. Kravchenko ◽  
V. I. Ponomaryov ◽  
V. I. Pustovoit ◽  
E. Rendon-Gonzalez
Keyword(s):  
Fractal Dimension ◽  
Texture Features ◽  
Ct Images ◽  
Lung Nodules

Artificial neural network based classification of lung nodules in CT images using intensity, shape and texture features

2019 ◽  
Vol 10 (10) ◽  
pp. 4135-4149 ◽  
Author(s):  
Furqan Shaukat ◽  
Gulistan Raja ◽  
Rehan Ashraf ◽  
Shehzad Khalid ◽  
Mudassar Ahmad ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Texture Features ◽  
Ct Images ◽  
Lung Nodules ◽  
Artificial Neural

GSA Based Classification of Lung Nodules in CT Images

2017 ◽  
Author(s):  
L. Maria Jenifer ◽  
T. Sathiya ◽  
B Sathiyabhama
Keyword(s):  
Ct Images ◽  
Lung Nodules

scholarly journals VGG19 Network Assisted Joint Segmentation and Classification of Lung Nodules in CT Images

Diagnostics ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2208
Author(s):  
Muhammad Attique Khan ◽  
Venkatesan Rajinikanth ◽  
Suresh Chandra Satapathy ◽  
David Taniar ◽  
Jnyana Ranjan Mohanty ◽  
...  
Keyword(s):  
Lung Nodule ◽  
Ct Images ◽  
Lung Nodules ◽  
Detection Accuracy ◽  
Learning Framework ◽  
Pet Ct ◽  
Nodule Mining ◽  
Serially Concatenated ◽  
Occurrence Matrix

Pulmonary nodule is one of the lung diseases and its early diagnosis and treatment are essential to cure the patient. This paper introduces a deep learning framework to support the automated detection of lung nodules in computed tomography (CT) images. The proposed framework employs VGG-SegNet supported nodule mining and pre-trained DL-based classification to support automated lung nodule detection. The classification of lung CT images is implemented using the attained deep features, and then these features are serially concatenated with the handcrafted features, such as the Grey Level Co-Occurrence Matrix (GLCM), Local-Binary-Pattern (LBP) and Pyramid Histogram of Oriented Gradients (PHOG) to enhance the disease detection accuracy. The images used for experiments are collected from the LIDC-IDRI and Lung-PET-CT-Dx datasets. The experimental results attained show that the VGG19 architecture with concatenated deep and handcrafted features can achieve an accuracy of 97.83% with the SVM-RBF classifier.

Multi-Class SVMs Based on Priority for Computer-Aided Diagnosis of Lung Nodules in CT Images

2011 ◽  
Vol 291-294 ◽  
pp. 2742-2745
Author(s):  
Qing Zhu Wang ◽  
Xin Zhu Wang ◽  
Ji Song Bie ◽  
Bin Wang
Keyword(s):  
Support Vector Machines ◽  
Sensitivity And Specificity ◽  
Ct Images ◽  
Computer Aided Diagnosis ◽  
Least Square ◽  
Support Vector ◽  
Lung Nodules ◽  
Vector Machines ◽  
Computer Aided ◽  
Aided Diagnosis

A priority based ‘One against all (OAA)’ Multi-class Least Square-Support Vector Machines is designed to remove the unclassifiable regions exist in basic OAA. POAA develops the sensitivity and specificity in Computer-aided Diagnosis (CAD) for detection of lung nodules.

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