Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/59306
Title: Development of Debiasing Technique for Lung Nodule Chest X-ray Datasets to Generalize Deep Learning Models
Contributor(s): Horry, Michael J (author); Chakraborty, Subrata  (author)orcid ; Pradhan, Biswajeet (author); Paul, Manoranjan (author); Zhu, Jing (author); Loh, Hui Wen (author); Barua, Prabal Datta (author); Acharya, U Rajendra (author)
Publication Date: 21-Jul-2023
Publisher: MDPI AG
Place of Publication: Switzerland
Open Access: Yes
DOI: 10.3390/s23146585
Handle Link: https://hdl.handle.net/1959.11/59306
ISSN: 1424-8220
1424-8239
Source of Publication: Sensors, 23(14), p. 1-21
Abstract: 

Screening programs for early lung cancer diagnosis are uncommon, primarily due to the challenge of reaching at-risk patients located in rural areas far from medical facilities. To overcome this obstacle, a comprehensive approach is needed that combines mobility, low cost, speed, accuracy, and privacy. One potential solution lies in combining the chest X-ray imaging mode with federated deep learning, ensuring that no single data source can bias the model adversely. This study presents a pre-processing pipeline designed to debias chest X-ray images, thereby enhancing internal classification and external generalization. The pipeline employs a pruning mechanism to train a deep learning model for nodule detection, utilizing the most informative images from a publicly available lung nodule X-ray dataset. Histogram equalization is used to remove systematic differences in image brightness and contrast. Model training is then performed using combinations of lung field segmentation, close cropping, and rib/bone suppression. The resulting deep learning models, generated through this pre-processing pipeline, demonstrate successful generalization on an independent lung nodule dataset. By eliminating confounding variables in chest X-ray images and suppressing signal noise from the bone structures, the proposed deep learning lung nodule detection algorithm achieves an external generalization accuracy of 89%. This approach paves the way for the development of a low-cost and accessible deep learning-based clinical system for lung cancer screening.

Publication Type: Journal Article
Fields of Research (FoR) 2020: 4601 Applied computing
Socio-Economic Objective (SEO) 2020: tbd
Access Rights: UNE Green
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Peer Reviewed: Yes
Appears in Collections:Import

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