| Autor: |
Devnath L; School of Information and Physical Sciences, The University of Newcastle, Newcastle, NSW 2308, Australia., Summons P; School of Information and Physical Sciences, The University of Newcastle, Newcastle, NSW 2308, Australia., Luo S; School of Information and Physical Sciences, The University of Newcastle, Newcastle, NSW 2308, Australia., Wang D; Quantitative Imaging, CSIRO Data61, Marsfield, Sydney, NSW 2122, Australia., Shaukat K; School of Information and Physical Sciences, The University of Newcastle, Newcastle, NSW 2308, Australia.; Department of Data Science, University of the Punjab, Lahore 54890, Pakistan., Hameed IA; Department of ICT and Natural Sciences, Norwegian University of Science and Technology, 7491 Trondheim, Norway., Aljuaid H; Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University (PNU), P.O. Box 84428, Riyadh 11671, Saudi Arabia. |
| Abstrakt: |
Computer-aided diagnostic (CAD) systems can assist radiologists in detecting coal workers' pneumoconiosis (CWP) in their chest X-rays. Early diagnosis of the CWP can significantly improve workers' survival rate. The development of the CAD systems will reduce risk in the workplace and improve the quality of chest screening for CWP diseases. This systematic literature review (SLR) amis to categorise and summarise the feature extraction and detection approaches of computer-based analysis in CWP using chest X-ray radiographs (CXR). We conducted the SLR method through 11 databases that focus on science, engineering, medicine, health, and clinical studies. The proposed SLR identified and compared 40 articles from the last 5 decades, covering three main categories of computer-based CWP detection: classical handcrafted features-based image analysis, traditional machine learning, and deep learning-based methods. Limitations of this review and future improvement of the review are also discussed. |
