P62.05 Identifying Risk-Factors for Lung Cancer Diagnosis After Detection of Incidental Lung Nodules

2021 ◽  
Vol 16 (10) ◽  
pp. S1178-S1179
Author(s):  
W. Liao ◽  
M. Smeltzer ◽  
N. Faris ◽  
C. Fehnel ◽  
J. Goss ◽  
...  
Keyword(s):  
Risk Factors ◽  
Lung Cancer ◽  
Cancer Diagnosis ◽  
Lung Nodules ◽  
Lung Cancer Diagnosis

scholarly journals An Efficient DA-Net Architecture for Lung Nodule Segmentation

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1457
Author(s):  
Muazzam Maqsood ◽  
Sadaf Yasmin ◽  
Irfan Mehmood ◽  
Maryam Bukhari ◽  
Mucheol Kim
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Ct Scan ◽  
Cancer Diagnosis ◽  
Lung Nodule ◽  
Search Space ◽  
Lung Nodules ◽  
Suggested Model ◽  
Lung Cancer Diagnosis ◽  
Segmentation Framework

A typical growth of cells inside tissue is normally known as a nodular entity. Lung nodule segmentation from computed tomography (CT) images becomes crucial for early lung cancer diagnosis. An issue that pertains to the segmentation of lung nodules is homogenous modular variants. The resemblance among nodules as well as among neighboring regions is very challenging to deal with. Here, we propose an end-to-end U-Net-based segmentation framework named DA-Net for efficient lung nodule segmentation. This method extracts rich features by integrating compactly and densely linked rich convolutional blocks merged with Atrous convolutions blocks to broaden the view of filters without dropping loss and coverage data. We first extract the lung’s ROI images from the whole CT scan slices using standard image processing operations and k-means clustering. This reduces the search space of the model to only lungs where the nodules are present instead of the whole CT scan slice. The evaluation of the suggested model was performed through utilizing the LIDC-IDRI dataset. According to the results, we found that DA-Net showed good performance, achieving an 81% Dice score value and 71.6% IOU score.

scholarly journals Lung Cancer Diagnosis by Fine Needle Aspiration Is Associated With Reduction in Resection of Nonmalignant Lung Nodules

2017 ◽  
Vol 103 (6) ◽  
pp. 1795-1801 ◽  
Author(s):  
Julie A. Barta ◽  
Claudia I. Henschke ◽  
Raja M. Flores ◽  
Rowena Yip ◽  
David F. Yankelevitz ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Diagnosis ◽  
Fine Needle Aspiration ◽  
Needle Aspiration ◽  
Lung Nodules ◽  
Fine Needle ◽  
Lung Cancer Diagnosis

Lung Cancer Diagnosis from Computer Tomography Images Using Region Based Segmentation and Fuzzy Logic

2020 ◽  
Vol 17 (4) ◽  
pp. 1898-1905
Author(s):  
P. Mangayarkarasi ◽  
B. Pugazhenthi
Keyword(s):  
Lung Cancer ◽  
Cancer Diagnosis ◽  
Fuzzy Inference ◽  
Early Stage ◽  
Control Process ◽  
Bone Cancer ◽  
Lung Nodules ◽  
Surgical Removal ◽  
Inference System ◽  
Lung Cancer Diagnosis

Lungs are the essential organs for respiration (inspiration and expiration) situated at thoracic cavity. Today, the lung cancer is serious disease in the world causing large number of deaths. The cells of all living organisms normally divide and grow in a control manner. When this control process is lost and tissues start expands then the situation is called cancer. Among the various cancers like bone cancer, breast cancer, blood cancer etc., the lung cancer is the most deadly one. The most preferred option for treating the lung cancer in the final stage is surgical removal of the diseased lung. Hence it is necessary to detect the lung cancer at an early stage to limit the danger. In this project the lung cancer diagnosis system based on Fuzzy Inference System (FIS) is proposed to detect the lung cancer at the early stage. The FIS plays a vital role in the medical field to provide medical assistance to the radiologist to diagnose the abnormality in the medical images. The proposed system first segments the suspected lung nodules from the input CT lung image using region based segmentation and classifies the suspected nodules as either benign (normal) or Malignant (cancerous) based on the feature extraction. Then the extracted features are given to the input of FIS. The Fuzzy system finds the severity of the suspected lung nodules based on IF-THEN rule.

Computer-Aided Lung Cancer Diagnosis Approaches Based on Deep Learning

2018 ◽  
Vol 30 (1) ◽  
pp. 90 ◽  
Author(s):  
Peng Zhang ◽  
Xinnan Xu ◽  
Hongwei Wang ◽  
Yuanli Feng ◽  
Haozhe Feng ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Cancer Diagnosis ◽  
Lung Cancer Diagnosis ◽  
Computer Aided

Biased Lung Cancer Risk Perceptions: Smokers are Misinformed

2018 ◽  
Vol 238 (5) ◽  
pp. 395-421 ◽  
Author(s):  
Nicolas R. Ziebarth
Keyword(s):  
Lung Cancer ◽  
Cancer Risk ◽  
Cancer Diagnosis ◽  
Mortality Risk ◽  
Risk Perceptions ◽  
Lung Cancer Risk ◽  
Lifetime Risk ◽  
Lung Cancer Diagnosis ◽  
Cancer Risk Perceptions ◽  
Overall Mortality

Abstract This paper empirically investigates biased beliefs about the risks of smoking. First, it confirms the established tendency of people to overestimate the lifetime risk of a smoker to contract lung cancer. In this paper’s survey, almost half of all respondents overestimate this risk. However, 80% underestimate lung cancer deadliness. In reality, less than one in five patients survive five years after a lung cancer diagnosis. Due to the broad underestimation of the lung cancer deadliness, the lifetime risk of a smoker to die of lung cancer is underestimated by almost half of all respondents. Smokers who do not plan to quit are significantly more likely to underestimate this overall mortality risk.

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