Abstract | ||
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Chest radiographs are widely used in the medical domain and at present, chest X-radiation particularly plays an important role in the diagnosis of medical conditions such as pneumonia and COVID-19 disease. The recent developments of deep learning techniques led to a promising performance in medical image classification and prediction tasks. With the availability of chest X-ray datasets and emerging trends in data engineering techniques, there is a growth in recent related publications. Recently, there have been only a few survey papers that addressed chest X-ray classification using deep learning techniques. However, they lack the analysis of the trends of recent studies. This systematic review paper explores and provides a comprehensive analysis of the related studies that have used deep learning techniques to analyze chest X-ray images. We present the state-of-the-art deep learning based pneumonia and COVID-19 detection solutions, trends in recent studies, publicly available datasets, guidance to follow a deep learning process, challenges and potential future research directions in this domain. The discoveries and the conclusions of the reviewed work have been organized in a way that researchers and developers working in the same domain can use this work to support them in taking decisions on their research. |
Year | DOI | Venue |
---|---|---|
2022 | 10.1016/j.asoc.2022.109319 | Applied Soft Computing |
Keywords | DocType | Volume |
Respiratory diseases,Radiography,Pneumonia,COVID-19,Convolutional Neural networks,Computer-aided diagnostics,Medical image processing,Chest radiography | Journal | 126 |
ISSN | Citations | PageRank |
1568-4946 | 0 | 0.34 |
References | Authors | |
0 | 6 |
Name | Order | Citations | PageRank |
---|---|---|---|
Dulani Meedeniya | 1 | 0 | 0.34 |
Hashara Kumarasinghe | 2 | 0 | 0.34 |
Shammi Kolonne | 3 | 0 | 0.34 |
Chamodi Fernando | 4 | 0 | 0.34 |
Isabel De la Torre Díez | 5 | 0 | 0.34 |
Gonçalo Marques | 6 | 0 | 0.34 |