Detecting pulmonary diseases using deep features in X-ray images

PATTERN RECOGNITION

卷:119

Article

Vieira, Pablo^1,4  Sousa, Orrana¹  Magalhaes, Deborah²  Rabelo, Ricardo³  Silva, Romuere^1,2,3 

[1] Fed Univ Piau, Elect Engn Dept, Picos, Brazil

[2] Fed Univ Piau, Informat Syst Dept, Picos, Brazil

[3] Fed Univ Piau, Comp Sci Dept, Teresina, Brazil

[4] Maida Hlth, Dev & Res, Teresina, Brazil

关键词: COVID-19;    X-ray;    Deep learning;    Pre-processing;   

DOI  :  10.1016/j.patcog.2021.108081

来源: Elsevier

【 摘 要 】

COVID-19 leads to radiological evidence of lower respiratory tract lesions, which support analysis to screen this disease using chest X-ray. In this scenario, deep learning techniques are applied to detect COVID-19 pneumonia in X-ray images, aiding a fast and precise diagnosis. Here, we investigate seven deep learning architectures associated with data augmentation and transfer learning techniques to detect different pneumonia types. We also propose an image resizing method with the maximum window func-tion that preserves anatomical structures of the chest. The results are promising, reaching an accuracy of 99.8% considering COVID-19, normal, and viral and bacterial pneumonia classes. The differentiation between viral pneumonia and COVID-19 achieved an accuracy of 99.8%, and 99.9% of accuracy between COVID-19 and bacterial pneumonia. We also evaluated the impact of the proposed image resizing method on classification performance comparing with the bilinear interpolation; this pre-processing increased the classification rate regardless of the deep learning architectures used. We c ompared our results with ten related works in the state-of-the-art using eight sets of experiments, which showed that the proposed method outperformed them in most cases. Therefore, we demonstrate that deep learning models trained with pre-processed X-ray images could precisely assist the specialist in COVID-19 detection. (c) 2021 Elsevier Ltd. All rights reserved.

【 授权许可】

Free