Lung Disease Detection and Classification with Deep Learning Approach

2021 ◽  
Author(s):  
Araya Chatchaiwatkul ◽  
Pasuk Phonsuphee ◽  
Yurananatul Mangalmurti ◽  
Naruemon Wattanapongsakorn
Keyword(s):  
Deep Learning ◽  
Lung Disease ◽  
Disease Detection ◽  
Learning Approach

scholarly journals Automatization and improvement of μCT analysis for murine lung disease models using a deep learning approach

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Gerald Birk ◽  
Marc Kästle ◽  
Cornelia Tilp ◽  
Birgit Stierstorfer ◽  
Stephan Klee
Keyword(s):  
Deep Learning ◽  
Lung Disease ◽  
Disease Models ◽  
Learning Approach ◽  
Murine Lung

Deep Learning for Lung Disease Detection from Chest X-Rays Images

2021 ◽  
pp. 239-254
Author(s):  
Ebenezer Jangam ◽  
Chandra Sekhara Rao Annavarapu ◽  
Mourad Elloumi
Keyword(s):  
Deep Learning ◽  
Lung Disease ◽  
Disease Detection ◽  
X Rays

Risk Prediction of Lung Disease Using Deep Learning Approach

2021 ◽  
pp. 462-471
Author(s):  
C. Harinath Reddy ◽  
B. V. Koushik Kumar ◽  
N. Sai Teja Varma ◽  
S. Vidya ◽  
P. Nagaraj ◽  
...  
Keyword(s):  
Deep Learning ◽  
Lung Disease ◽  
Risk Prediction ◽  
Learning Approach

scholarly journals A Survey of Deep Learning for Lung Disease Detection on Medical Images: State-of-the-Art, Taxonomy, Issues and Future Directions

2020 ◽  
Vol 6 (12) ◽  
pp. 131
Author(s):  
Stefanus Tao Hwa Kieu ◽  
Abdullah Bade ◽  
Mohd Hanafi Ahmad Hijazi ◽  
Hoshang Kolivand
Keyword(s):  
Deep Learning ◽  
Lung Disease ◽  
Recent Work ◽  
Lung Diseases ◽  
Data Augmentation ◽  
State Of The Art ◽  
Medical Images ◽  
Disease Detection ◽  
Future Directions ◽  
Survey Paper

The recent developments of deep learning support the identification and classification of lung diseases in medical images. Hence, numerous work on the detection of lung disease using deep learning can be found in the literature. This paper presents a survey of deep learning for lung disease detection in medical images. There has only been one survey paper published in the last five years regarding deep learning directed at lung diseases detection. However, their survey is lacking in the presentation of taxonomy and analysis of the trend of recent work. The objectives of this paper are to present a taxonomy of the state-of-the-art deep learning based lung disease detection systems, visualise the trends of recent work on the domain and identify the remaining issues and potential future directions in this domain. Ninety-eight articles published from 2016 to 2020 were considered in this survey. The taxonomy consists of seven attributes that are common in the surveyed articles: image types, features, data augmentation, types of deep learning algorithms, transfer learning, the ensemble of classifiers and types of lung diseases. The presented taxonomy could be used by other researchers to plan their research contributions and activities. The potential future direction suggested could further improve the efficiency and increase the number of deep learning aided lung disease detection applications.

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