Multilayer Fractional-Order Machine Vision Classifier for Rapid Typical Lung Diseases Screening on Digital Chest X-Ray Images
| Autor: | Chia-Hung Lin, Chien-Ming Li, Pi-Yun Chen, Neng-Sheng Pai, Jian-Xing Wu, Ying-Che Kuo |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
medicine.medical_specialty General Computer Science Pleural effusion Radiography Atelectasis 02 engineering and technology Digital image 020901 industrial engineering & automation Region of interest 0202 electrical engineering electronic engineering information engineering Medicine General Materials Science Lung cancer Lung business.industry General Engineering respiratory system medicine.disease respiratory tract diseases medicine.anatomical_structure Pneumothorax Multilayer machine vision classifier gray relational analysis 020201 artificial intelligence & image processing Radiology region of interest lcsh:Electrical engineering. Electronics. Nuclear engineering business Chest Radiography fractional-order convolution lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 8, Pp 105886-105902 (2020) |
| ISSN: | 2169-3536 |
| Popis: | Lung diseases can result in acute breathing problems and prevent the human body from acquiring enough oxygen. These diseases, such as pneumonia (P), pleural effusion (Ef), lung cancer, pneumothorax (Pt), pulmonary fibrosis (F), infiltration (In) and emphysema (E), adversely affect airways, alveoli, blood vessels, pleura and other parts of the respiratory system. The death rates of P and lung cancer are higher than those of other typical lung diseases. In visualization examination, chest radiography, such as anterior-posterior or lateral image viewing, is a straightforward approach used by clinicians/radiologists to diagnose and locate possible lung abnormalities rapidly. However, a chest X-ray image of patients may show multiple abnormalities associated with coexisting conditions, such as P, E, F, Pt, atelectasis, lung cancer or surgical interventions, which further complicate diagnosis. In addition, poor-quality X-ray images and manual inspection have limitations in digital image-automated classification. Hence, this study intends to propose a multilayer machine vision classifier to automatically identify the possible class of lung diseases within a bounding region of interest (ROI) on a chest X-ray image. For digital image texture analysis, a two-dimensional (2D) fractional-order convolution (FOC) operation with a fractional-order parameter, $v =0.3-0.5$ , is used to enhance the symptomatic feature and remove unwanted noises. Then, maximum pooling is performed to reduce the dimensions of feature patterns and accelerate complex computations. A multilayer machine vision classifier with radial Bayesian network and gray relational analysis is used to screen subjects with typical lung diseases. Anterior-posterior chest X-ray images from the NIH chest X-ray database (NIH Clinical Center) are enrolled. For digital chest X-ray images, with $K$ -fold cross-validation, the proposed multilayer machine vision classifier is applied to facilitate the diagnosis of typical lung diseases on specific bounding ROIs, as promising results with mean recall (%), mean precision (%), mean accuracy (%) and mean F1 score of 98.68%, 82.42%, 83.57% and 0.8981, respectively, for assessing the performance of proposed multilayer classifier for rapidly screening lung lesions on digital chest X-ray images. |
| Databáze: | OpenAIRE |
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