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https://hdl.handle.net/1959.11/43265
Title: | Deep Mining Generation of Lung Cancer Malignancy Models from Chest X-ray Images | Contributor(s): | Horry, Michael (author); Chakraborty, Subrata (author) ; Pradhan, Biswajeet (author); Paul, Manoranjan (author); Gomes, Douglas (author); Ul-Haq, Anwaar (author); Alamri, Abdullah (author) | Publication Date: | 2021-10 | Early Online Version: | 2021-10-07 | Open Access: | Yes | DOI: | 10.3390/s21196655 | Handle Link: | https://hdl.handle.net/1959.11/43265 | Abstract: | Lung cancer is the leading cause of cancer death and morbidity worldwide. Many studies have shown machine learning models to be effective in detecting lung nodules from chest X-ray images. However, these techniques have yet to be embraced by the medical community due to several practical, ethical, and regulatory constraints stemming from the "black-box" nature of deep learning models. Additionally, most lung nodules visible on chest X-rays are benign; therefore, the narrow task of computer vision-based lung nodule detection cannot be equated to automated lung cancer detection. Addressing both concerns, this study introduces a novel hybrid deep learning and decision tree-based computer vision model, which presents lung cancer malignancy predictions as interpretable decision trees. The deep learning component of this process is trained using a large publicly available dataset on pathological biomarkers associated with lung cancer. These models are then used to inference biomarker scores for chest X-ray images from two independent data sets, for which malignancy metadata is available. Next, multi-variate predictive models were mined by fitting shallow decision trees to the malignancy stratified datasets and interrogating a range of metrics to determine the best model. The best decision tree model achieved sensitivity and specificity of 86.7% and 80.0%, respectively, with a positive predictive value of 92.9%. Decision trees mined using this method may be considered as a starting point for refinement into clinically useful multi-variate lung cancer malignancy models for implementation as a workflow augmentation tool to improve the efficiency of human radiologists. | Publication Type: | Journal Article | Source of Publication: | Sensors, 21(19), p. 1-23 | Publisher: | MDPI AG | Place of Publication: | Switzerland | ISSN: | 1424-8220 1424-8239 |
Fields of Research (FoR) 2020: | 460102 Applications in health 461103 Deep learning 460308 Pattern recognition |
Socio-Economic Objective (SEO) 2020: | 209999 Other health not elsewhere classified 280115 Expanding knowledge in the information and computing sciences |
Peer Reviewed: | Yes | HERDC Category Description: | C1 Refereed Article in a Scholarly Journal |
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Appears in Collections: | Journal Article School of Science and Technology |
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openpublished/DeepChakraborty2021JournalArticle.pdf | Published version | 8.76 MB | Adobe PDF Download Adobe | View/Open |
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