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Chest X-ray Detection and Classification of Chest Abnormalities
This special issue belongs to the section “Medical Imaging and Theranostics“.
Special Issue Information
Dear Colleagues,
Chest radiography was one of the first and most diffuse imaging modalities to be performed, and it is still one of the most commonly used diagnostic methods. Due to its widespread diffusion, relative low cost, ease of performance and low radiant dose exposure, it represents one of the most common first-level examination used for the evaluation of chest diseases.
The recent and rapid development of new technologies, based firstly, for example, on computer-aided systems, and then on artificial intelligence technologies, has aided radiologists in the detection of imaging findings that could be misdiagnosed and in the definition of diagnosis in cases that may be difficult to understand.
This Special Issue aims to investigate the role of chest X-ray imaging in the detection and the classification of chest lesions, providing useful information on recent advancements in radiography and aiming to provide contributions which will improve clinical decision making and medical diagnoses.
Dr. Chiara Romei
Prof. Dr. Emanuele Neri
Guest Editors
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- chest X-ray
- artificial intelligence
- radiomics
- deep learning
- machine learning
- pleural effusion
- pneumothorax
- multiple masses/nodules
- lung consolidations
- lung cancers
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