Who knew Deep Learning can come so handy to us during this period of global crisis? There has yet been no vaccine or any effective treatment for the 2019 novel Coronavirus (COVID-19), but generative deep learning is helping in detecting and monitoring coronavirus patients by chest CT screening.

1. Detect COVID-19 with Deep
Learning- A survey on Deep
Learning for Pulmonary
Medical Imaging
By- Jumana Nadir Medium Article
Short Story

2. Introduction
● Who knew Deep Learning can come
so handy to us during this period of
global crisis?
● There has yet been no vaccine or
any effective treatment for the 2019
novel Coronavirus (COVID-19), but
generative deep learning is helping
in detecting and monitoring
coronavirus patients by
chest CT screening.

3. Survey on deep learning for pulmonary medical imaging
https://link.springer.com/article/10.1007/s11684-019-0726-4
● Survey Authors- Jiechao Ma, Yang Song, Xi Tian, Yiting Hua, Rongguo
Zhang, and Jianlin Wu.
● Published: 16 December 2019
● Abstract- Elaborates the state-of-the-art Deep Learning techniques used to
detect various lung diseases, Lung cancers, Pneumonia, Tuberculosis, and
its contribution to the classification, detection, and segmentation of
Pulmonary Medical Diseases.

4. Contents
● Overview Of Deep Learning.
● 3 important aspects of Medical Image Analysis.
● Deep Learning in Medical Pulmonary Image (Lung Cancer).
● Pneumonia, Tuberculosis, Interstitial Lung Diseases.
● Existing Datasets with download links.
● Conclusion.

5. Overview
● Modern lifestyle changes, pollution, and
global warming have attracted many
Respiratory Diseases which are life-
threatening.
● No signs during early stage.
● People miss this period of early treatment
and realize when it becomes serious.

6. Overview Of Deep Learning
●In medical imaging accurate diagnosis depends on image
collection and its computer-aided diagnosis (CAD)
●1993, first time Neural Networks were used, but were not
accepted because of computation requirements.
●Detection Criteria- Finding possible lesions and tumors
(region/organ suffering damage)
●Shape, size, density, textures

7. History

8. 3 important aspects of Medical Image Analysis.
1. Classification- Normal/Abnormal; Binary/Multiclass
2. Detection- Detect Region of Interest (ROI)
3. Segmentation- Segment meaningful parts- organs,
substructure, lesion, extract features

9. Deep Learning in Medical Pulmonary Image (Lung Cancer)
● Classify Malignant/ Benign using CNN and SVM.
● Detection using RCNN and DCNN (high sensitivity + precision required).
● Segmentation label- generate accurate voxel-level nodule segmentation.

10. Fatal Lung Diseases and their Diagnosis with Deep
Learning

11. Pulmonary Embolism
● About 650000 cases occur annually
● Artery in lung becomes partially or completely blocked.
● Doctors approach- Angiography
● DL- Neural Hypernetworks
● Knowledge base hybrid learning algorithms

12. Pneumonia
●Common among Children
●Early detection can save lot of lives
●X-ray examination is most common method
●Images are very similar
●Template matching algorithm are used in CUDA and CNN
architectures

13. Tuberculosis
● Respiratory tract disease, caused by pathogen ‘Mycobacterium tuberculosis’.
● Common methods are X-rays, patient’s signs and symptoms, mucus exam.
● Multi-instance learning combined with RNN’s achieved good results.

14. Interstitial Lung Diseases
● Heterogenous, non-neoplastic, non-infectious disease
● Contains abnormal imaging patterns
● Below models achieved good results

15. Existing Datasets with download links
● LIDC- Lung Image Database
Consortium- consists of chest
medical images of 1018 research
cases.
● LUNA16- it’s a subset of the above
dataset that contains low-dose lung
CT images.
● Pneumonia Dataset by National
Institutes of Health (size- 42GB).
● Tuberculosis Dataset by Shenzhen
Hospital (size- 4GB).
● Geneva Database has Interstitial
Lung Disease Dataset.
Performances of the two
pulmonary nodule datasets LIDC-
IDRI, and LUNA16-

16. Conclusion
● Lots of prospects with emerging Deep Learning technology.
● DL can transform Healthcare System
● Can Machines replace Doctors?

17. References
● Ma, J., Song, Y., Tian, X. et al. Survey on deep learning for pulmonary medical imaging. Front.
Med. (2019). https://doi.org/10.1007/s11684-019-0726-4
● Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Rajendra Acharya U. Automated detection of COVID-19
cases using deep neural networks with X-ray images [published online ahead of print, 2020 Apr 28]. Comput
Biol Med. 2020;103792. https://doi.org/10.1016/j.compbiomed.2020.103792
● Q. Dou, H. Chen, L. Yu, J. Qin, and P. Heng, “Multilevel Contextual 3-D CNNs for False Positive Reduction in
Pulmonary Nodule Detection,” in IEEE Transactions on Biomedical Engineering, vol. 64, no. 7, pp. 1558–1567, July
2017, 10.1109/TBME.2016.2613502
● Magalhães Barros Netto S, Corrêa Silva A, Acatauassú Nunes R, Gattass M. Automatic segmentation of lung
nodules with growing neural gas and support vector machine. Comput Biol Med 2012; 42(11): 1110–
1121https://doi.org/10.1016/j.compbiomed.2012.09.003
● Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. In:
International Conference on Medical image computing and computer-assisted intervention. Springer. 2015.
234–241 https://doi.org/10.1007/978-3-319-24574-4_28