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

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

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.

Classification of lung nodules based on CT images using squeeze-and-excitation network and aggregated residual transformations

2020 ◽  
Vol 125 (4) ◽  
pp. 374-383 ◽  
Author(s):  
Guobin Zhang ◽  
Zhiyong Yang ◽  
Li Gong ◽  
Shan Jiang ◽  
Lu Wang ◽  
...  
Keyword(s):  
Ct Images ◽  
Lung Nodules

Deep learning-based CAD schemes for the detection and classification of lung nodules from CT images: A survey

2020 ◽  
pp. 1-27
Author(s):  
Rekka Mastouri ◽  
Nawres Khlifa ◽  
Henda Neji ◽  
Saoussen Hantous-Zannad
Keyword(s):  
Deep Learning ◽  
Ct Images ◽  
Lung Nodules

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

Classification of Lung Nodules Based on GAN and 3D CNN

2020 ◽  
Author(s):  
Bin Sun ◽  
Fengyin Liu ◽  
Yusun Zhou ◽  
Shaolei Jin ◽  
Qiang Li ◽  
...  
Keyword(s):  
Lung Nodules ◽  
3D Cnn
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