scholarly journals Novel computer-aided diagnosis of lung cancer using bag of visual words to achieve high accuracy rates

2018 ◽  
Vol 2018 (12) ◽  
pp. 1941-1946 ◽  
Author(s):  
Thinkal Dayana Chellan ◽  
Agees Kumar Chellappan
Keyword(s):  
Lung Cancer ◽  
High Accuracy ◽  
Computer Aided Diagnosis ◽  
Bag Of Visual Words ◽  
Visual Words ◽  
Computer Aided ◽  
Diagnosis Of Lung Cancer ◽  
Accuracy Rates ◽  
Aided Diagnosis

Computer-aided diagnosis of lung cancer by helical CT screening: Retrospective analysis

Lung Cancer ◽  
2000 ◽  
Vol 29 (1) ◽  
pp. 241-242
Author(s):  
R Kakinuma ◽  
H Ohmatsu ◽  
M Kaneko ◽  
M Kusumoto ◽  
E Kenji ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Retrospective Analysis ◽  
Helical Ct ◽  
Computer Aided Diagnosis ◽  
Ct Screening ◽  
Computer Aided ◽  
Diagnosis Of Lung Cancer ◽  
Aided Diagnosis

Computer-Aided Diagnosis of Lung Cancer in Magnetic Resonance Imaging Exams

2019 ◽  
pp. 121-127
Author(s):  
Victor Francisco ◽  
Marcel Koenigkam-Santos ◽  
Danilo Tadao Wada ◽  
José Raniery Ferreira Junior ◽  
Alexandre Todorovic Fabro ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Lung Cancer ◽  
Magnetic Resonance ◽  
Computer Aided Diagnosis ◽  
Resonance Imaging ◽  
Computer Aided ◽  
Diagnosis Of Lung Cancer ◽  
Aided Diagnosis

scholarly journals Potential Lung Nodules Identification for Characterization by Variable Multistep Threshold and Shape Indices from CT Images

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Saleem Iqbal ◽  
Khalid Iqbal ◽  
Fahim Arif ◽  
Arslan Shaukat ◽  
Aasia Khanum
Keyword(s):  
Lung Cancer ◽  
Ct Scan ◽  
Imaging Modality ◽  
Image Database ◽  
Computer Aided Diagnosis ◽  
Features Extraction ◽  
Lung Nodules ◽  
Computer Aided ◽  
Diagnosis Of Lung Cancer ◽  
Aided Diagnosis

Computed tomography (CT) is an important imaging modality. Physicians, surgeons, and oncologists prefer CT scan for diagnosis of lung cancer. However, some nodules are missed in CT scan. Computer aided diagnosis methods are useful for radiologists for detection of these nodules and early diagnosis of lung cancer. Early detection of malignant nodule is helpful for treatment. Computer aided diagnosis of lung cancer involves lung segmentation, potential nodules identification, features extraction from the potential nodules, and classification of the nodules. In this paper, we are presenting an automatic method for detection and segmentation of lung nodules from CT scan for subsequent features extraction and classification. Contribution of the work is the detection and segmentation of small sized nodules, low and high contrast nodules, nodules attached with vasculature, nodules attached to pleura membrane, and nodules in close vicinity of the diaphragm and lung wall in one-go. The particular techniques of the method are multistep threshold for the nodule detection and shape index threshold for false positive reduction. We used 60 CT scans of “Lung Image Database Consortium-Image Database Resource Initiative” taken by GE medical systems LightSpeed16 scanner as dataset and correctly detected 92% nodules. The results are reproducible.

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