Automatic Detection and Classification of Solitary Pulmonary Nodules from Lung CT Images

2014 ◽  
Author(s):  
Jhilam Mukherjee ◽  
Amlan Chakrabarti ◽  
Soharab Hossain Shaikh ◽  
Madhuchanda Kar
Keyword(s):  
Pulmonary Nodules ◽  
Automatic Detection ◽  
Ct Images ◽  
Solitary Pulmonary Nodules ◽  
Lung Ct

Classification of pulmonary nodules in lung CT images using shape and texture features

2016 ◽  
Author(s):  
Ashis Kumar Dhara ◽  
Sudipta Mukhopadhyay ◽  
Anirvan Dutta ◽  
Mandeep Garg ◽  
Niranjan Khandelwal ◽  
...  
Keyword(s):  
Pulmonary Nodules ◽  
Texture Features ◽  
Ct Images ◽  
Lung Ct

scholarly journals A Combination of Shape and Texture Features for Classification of Pulmonary Nodules in Lung CT Images

2016 ◽  
Vol 29 (4) ◽  
pp. 466-475 ◽  
Author(s):  
Ashis Kumar Dhara ◽  
Sudipta Mukhopadhyay ◽  
Anirvan Dutta ◽  
Mandeep Garg ◽  
Niranjan Khandelwal
Keyword(s):  
Pulmonary Nodules ◽  
Texture Features ◽  
Ct Images ◽  
Lung Ct

Measurement of spiculation index in 3D for solitary pulmonary nodules in volumetric lung CT images

2013 ◽  
Author(s):  
Ashis Kumar Dhara ◽  
Sudipta Mukhopadhyay ◽  
Naved Alam ◽  
Niranjan Khandelwal
Keyword(s):  
Pulmonary Nodules ◽  
Ct Images ◽  
Solitary Pulmonary Nodules ◽  
Lung Ct

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

2019 ◽  
Vol 9 (1) ◽  
pp. 1808-1819 ◽  
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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