| Autor: |
Devnath L; School of Information and Physical Sciences, The University of Newcastle, Callaghan, NSW 2308, Australia.; British Columbia Cancer Research Centre, Vancouver, BC V5Z 1L3, Canada., Luo S; School of Information and Physical Sciences, The University of Newcastle, Callaghan, NSW 2308, Australia., Summons P; School of Information and Physical Sciences, The University of Newcastle, Callaghan, NSW 2308, Australia., Wang D; Quantitative Imaging, CSIRO Data61, Marsfield, NSW 2122, Australia., Shaukat K; School of Information and Physical Sciences, The University of Newcastle, Callaghan, 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., Alrayes FS; Information Systems Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University (PNU), P.O. Box 84428, Riyadh 11671, Saudi Arabia. |
| Abstrakt: |
Globally, coal remains one of the natural resources that provide power to the world. Thousands of people are involved in coal collection, processing, and transportation. Particulate coal dust is produced during these processes, which can crush the lung structure of workers and cause pneumoconiosis. There is no automated system for detecting and monitoring diseases in coal miners, except for specialist radiologists. This paper proposes ensemble learning techniques for detecting pneumoconiosis disease in chest X-ray radiographs (CXRs) using multiple deep learning models. Three ensemble learning techniques (simple averaging, multi-weighted averaging, and majority voting (MVOT)) were proposed to investigate performances using randomised cross-folds and leave-one-out cross-validations datasets. Five statistical measurements were used to compare the outcomes of the three investigations on the proposed integrated approach with state-of-the-art approaches from the literature for the same dataset. In the second investigation, the statistical combination was marginally enhanced in the ensemble of multi-weighted averaging on a robust model, CheXNet. However, in the third investigation, the same model elevated accuracies from 87.80 to 90.2%. The investigated results helped us identify a robust deep learning model and ensemble framework that outperformed others, achieving an accuracy of 91.50% in the automated detection of pneumoconiosis. |
