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

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.

scholarly journals Reducing False-Positives in Lung Nodules Detection Using Balanced Datasets

2021 ◽  
Vol 9 ◽  
Author(s):  
Jinglun Liang ◽  
Guoliang Ye ◽  
Jianwen Guo ◽  
Qifan Huang ◽  
Shaohui Zhang
Keyword(s):  
Lung Cancer ◽  
Early Stage ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
False Positives ◽  
Classification Systems ◽  
Cad System ◽  
Nodule Detection ◽  
Diagnosis Of Lung Cancer ◽  
Lung Ct

Malignant pulmonary nodules are one of the main manifestations of lung cancer in early CT image screening. Since lung cancer may have no early obvious symptoms, it is important to develop a computer-aided detection (CAD) system to assist doctors to detect the malignant pulmonary nodules in the early stage of lung cancer CT diagnosis. Due to the recent successful applications of deep learning in image processing, more and more researchers have been trying to apply it to the diagnosis of pulmonary nodules. However, due to the ratio of nodules and non-nodules samples used in the training and testing datasets usually being different from the practical ratio of lung cancer, the CAD classification systems may easily produce higher false-positives while using this imbalanced dataset. This work introduces a filtering step to remove the irrelevant images from the dataset, and the results show that the false-positives can be reduced and the accuracy can be above 98%. There are two steps in nodule detection. Firstly, the images with pulmonary nodules are screened from the whole lung CT images of the patients. Secondly, the exact locations of pulmonary nodules will be detected using Faster R-CNN. Final results show that this method can effectively detect the pulmonary nodules in the CT images and hence potentially assist doctors in the early diagnosis of lung cancer.

scholarly journals Hybrid Scheme for Automated Classification of Pulmonary Nodules Using PET/CT Images and Patient Information

2020 ◽  
Vol 10 (12) ◽  
pp. 4225
Author(s):  
Ayumi Yamada ◽  
Atsushi Teramoto ◽  
Masato Hoshi ◽  
Hiroshi Toyama ◽  
Kazuyoshi Imaizumi ◽  
...  
Keyword(s):  
Patient Information ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
Machine Learning Algorithms ◽  
Automated Classification ◽  
Hybrid Scheme ◽  
Laboratory Test Results ◽  
Pet Ct ◽  
Functional Features

The classification of pulmonary nodules using computed tomography (CT) and positron emission tomography (PET)/CT is often a hard task for physicians. To this end, in our previous study, we developed an automated classification method using PET/CT images. In actual clinical practice, in addition to images, patient information (e.g., laboratory test results) is available and may be useful for automated classification. Here, we developed a hybrid scheme for automated classification of pulmonary nodules using these images and patient information. We collected 36 conventional CT images and PET/CT images of patients who underwent lung biopsy following bronchoscopy. Patient information was also collected. For classification, 25 shape and functional features were first extracted from the images. Benign and malignant nodules were identified using machine learning algorithms along with the images’ features and 17 patient-information-related features. In the leave-one-out cross-validation of our hybrid scheme, 94.4% of malignant nodules were identified correctly, and 77.7% of benign nodules were diagnosed correctly. The hybrid scheme performed better than that of our previous method that used only image features. These results indicate that the proposed hybrid scheme may improve the accuracy of malignancy analysis.

Quantitative evaluation of margin sharpness of pulmonary nodules in lung CT images

2016 ◽  
Vol 10 (9) ◽  
pp. 631-637 ◽  
Author(s):  
Ashis Kumar Dhara ◽  
Satrajit Chakrabarty ◽  
Niranjan Khandelwal ◽  
Mandeep Garg ◽  
Sudipta Mukhopadhyay
Keyword(s):  
Quantitative Evaluation ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
Lung Ct
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