Luns - Automatic lungs segmentation through neural network
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- Irene Canavesi, B.Sc. student in Biomedical Engineering - Sara Caramaschi, B.Sc. student in Biomedical Engineering Lung cancer is one of the most frequently diagnosed cancer forms, with a mortality of 84.2% in 2018. Our project focuses on shortening diagnosis time and improving accuracy in the overall detection of this disease. We implemented a convolutional neural network capable of automatically identifying lungs on a CT image. Segmentation is a necessary first step for the development of an algorithm capable of identifying and classifying the tumor mass since errors in the ROI identification can lead to errors in the tumor mass recognition. The network architecture follows the structure of a preexisting network, the U-Net that performs well on medical images. We reached a very good test accuracy of 99.63%: the strength of our work lies in the large number of CT images of both healthy and sick patients, used for the training and validation of the network.
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![Lung Cancer
Mortality rate: 84.2 %
2.09 millions registered cases
1.76 millions deaths in 2018[1]
[1] Globocan. (2018). Lung Fact Sheet. International Agency for Research on Cancer, WHO, 876, 2018–2019. http://gco.iarc.fr/today
2Irene Canavesi, Sara Caramaschi LunS](https://image.slidesharecdn.com/virtualngcsarairene-200731085733/85/Luns-Automatic-lungs-segmentation-through-neural-network-2-320.jpg)
![4
Lung segmentation U-Net[2]
Diversified
dataset[3][4]
Irene Canavesi, Sara Caramaschi LunS
[2] Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation.
https://doi.org/10.1007/978-3-319-24574-4_28
[3] https://cdas.cancer.gov/nlst/
[4] Aerts, H. J. W. L., Wee, L., Rios Velazquez, E., Leijenaar, R. T. H., Parmar, C., Grossmann, P. Lambin, P. (2019). Data From NSCLC-Radiomics [Data
set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2015.PF0M9RE
Our idea](https://image.slidesharecdn.com/virtualngcsarairene-200731085733/85/Luns-Automatic-lungs-segmentation-through-neural-network-4-320.jpg)
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medical industry is also automating itself with the
aid of image recognition and data analytics. Lung
cancer is one of the most common diseases for
human beings everywhere throughout the world.
Lung cancer is a disease which arises due to growth
of unwanted tissues in the lung and this growth
which spreads beyond the lung are named as
metastasis which spreads into other parts of the
body.
The objective of our project is to inspect accuracy
ratio of two classifiers which is Support Vector
Machine (SVM), and K Nearest Neighbour
(KNN) on common platform that classify lung
cancer in early stage so that many lives can be
saving. The experimental results show that KNN
gives the best result Than SVM. This report
discusses the Implementation details of our
project.
We have done data preprocessing, data cleaning
and implements machine leaning algorithm for
prediction of lung cancer at early stages through
their symptoms. We have used both classification
algorithms to find or predict the accuracy ratio.
Lung cancer is identified as the most common
cancer in the world that causes death. Early
detection has the ability to reduce deaths by 20%.
In the current clinical process, radiologists use
Computed Tomography (CT) scans to identify
lung cancer in early stages. Radiologists do so by
searching for regions called ‘nodules’, which
correspond to abnormal cell growths. But
identifying process is time consuming, laborious
and depends on the experience of the radiologist.
Hence an intelligent system to automatically
assess whether a patient is prone to have a lung
cancer is a need.
This paper presents a novel method which use
deep learning, namely convolutional neural
networks (CNNs) to identify whether a given CT scan shows evidence of lung cancer or not. The
implementation uses a combination of classical
feature-based candidate detection with modern
deep-learning architectures to generate excellent
results better than either of the methods. The
overall implementation consists of two stages.
Nodule Regions-of-Interest (ROI) extraction and
cancer classification. In nodule ROI extraction
stage, we select top most candidate regions as
nodules. A combination of rule based image
processing method and a 2D CNN was used for
this stage. In the cancer classification stage, we
estimate the malignancy of each nodule regions
and hence label the whole CT scan as cancerous
or non-cancerous. A combination of feature based
eXtreme Gradient Boosting (XGBoost) classifier
and 3D CNN was used for this stage. The LUNA
dataset and LIDC dataset were used for both
training and testing. The results were clearly
demonstrated promising classification
performance. The sensitivity, accuracy and
specificity values obtained for
Icru 89 
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Gaussian Multi-Scale Feature Disassociation Screening in Tuberculosise
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The Clinical use of Artificial Intelligence in the Analysis of Chest Radiogra...
The Clinical use of Artificial Intelligence in the Analysis of Chest Radiogra...semualkaira Computer-assisted detection (CAD) systems
based on artificial intelligence (AI) utilizing convolutional neural
networks (CNN) have demonstrated successful outcomes in diagnosing lung lesions in several studies. This study aims to report
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efficacy in commonly encountered cases and categorizing the lung
nodules that they can effectively detect.
The Clinical use of Artificial Intelligence in the Analysis of Chest Radiogra...
The Clinical use of Artificial Intelligence in the Analysis of Chest Radiogra...semualkaira Computer-assisted detection (CAD) systems
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deep learning applications in medical image analysis brain tumor
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Luns - Automatic lungs segmentation through neural network
- 1. LunS Lung Segmentation through Artificial Neural Networks virtual NGC - 22.07.20 Caramaschi Sara Canavesi Irene Marco D. Santambrogio Eleonora D’Arnese
- 2. Lung Cancer Mortality rate: 84.2 % 2.09 millions registered cases 1.76 millions deaths in 2018[1] [1] Globocan. (2018). Lung Fact Sheet. International Agency for Research on Cancer, WHO, 876, 2018–2019. http://gco.iarc.fr/today 2Irene Canavesi, Sara Caramaschi LunS
- 3. Long diagnosis time Automatization of the process 3 LunSIrene Canavesi, Sara Caramaschi Problem & applied solution Screening exam Biopsy
- 4. 4 Lung segmentation U-Net[2] Diversified dataset[3][4] Irene Canavesi, Sara Caramaschi LunS [2] Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. https://doi.org/10.1007/978-3-319-24574-4_28 [3] https://cdas.cancer.gov/nlst/ [4] Aerts, H. J. W. L., Wee, L., Rios Velazquez, E., Leijenaar, R. T. H., Parmar, C., Grossmann, P. Lambin, P. (2019). Data From NSCLC-Radiomics [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2015.PF0M9RE Our idea
- 5. 5 Accuracy: 99.73 % Loss: 0.0073 Irene Canavesi, Sara Caramaschi LunS Our results
- 6. 6Irene Canavesi, Sara Caramaschi LunS Lung segmentation Tumor mass identification Conclusion & Future work
- 7. Thank you for your attention Sara Caramaschi [email protected] Eleonora D’Arnese [email protected] Marco D. Santambrogio [email protected] Irene Canavesi [email protected]