A Deep Learning Based Suggested Model to Detect Necrotising Enterocolitis in Abdominal Radiography Images

Conference paper


Van Druten, J., Sharif, S., Chan, S. S., Chong, C. and Abdalla, H. 2019. A Deep Learning Based Suggested Model to Detect Necrotising Enterocolitis in Abdominal Radiography Images. IEEE International Conference on Computing, Electronics & Communications Engineering 2019 (IEEE iCCECE '19) . London Metropolitan University, London, UK 22 - 23 Aug 2019 IEEE. https://doi.org/10.1109/iCCECE46942.2019.8941615
AuthorsVan Druten, J., Sharif, S., Chan, S. S., Chong, C. and Abdalla, H.
TypeConference paper
Abstract

Despite decades of exploration into necrotising
enterocolitis (NEC), we still lack the capacity to accurately diagnose the disease to improve outcomes in its management. Existing diagnostics struggle to delineate NEC from other neonatal intestinal diseases; it is also unable to highlight those likely to deteriorate to needing emergency life-saving surgery before it is too late. The diagnosis of NEC is heavily dependent on interpretation of radiological findings, especially abdominal radiography (AR) and abdominal ultrasound (AUS). Interexpert variability in interpreting AR imaging, and in the case of AUS, performing and interpreting the test, remains an unresolved challenge. With the compounding impact of the shrinking radiology workforce, a novel approach is imperative. Computer assisted detection (CAD) and classification of abnormal pathology in medical imaging is a rapidly evolving field of clinical and biomedical research. This technology is widely used as a preliminary screening tool. This research paper
proposes a deep learning-based model to classify AR images in an automated manner, generating class activation maps (CAM) from various imaging features consistent with NEC pathology, as agreed by expert consensus papers (in neonatology and paediatric radiology). It also compares it with conventional machine learning methods. The suggested model aims to
produce heatmaps for various imaging features to highlight NEC pathology in AR (or in future AUS). Once the model is trained, validation is done through quantitative measures and visually by the attending radiologist (clinician) reviewing the validity of the colour maps highlighting the pathology of the AR image (future extension to AUS). As the volume of imaging data is increasing year by year, CAD can be a key strategy to assist radiology departments meet service needs. This technology can greatly assist in screening for NEC, improving the detection of NEC and potentially aid in the earlier identification of disease. Furthermore, it can fast track research cost effectively by creating big data through the automatic labeling of imaging data to create big-data for NEC databases.

KeywordsComputer assisted detection; Necrotizing enterocolitis; Abdominal radiograph; Abdominal X-ray; CAD; LBP; SVM; CNN; Machine learning; artificial intelligence; Deep learning; Ensemble Modelling; Class activation map
Year2019
ConferenceIEEE International Conference on Computing, Electronics & Communications Engineering 2019 (IEEE iCCECE '19)
PublisherIEEE
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Anyone
Publication dates
Online26 Dec 2019
Publication process dates
AcceptedJul 2019
Deposited08 Aug 2019
Book titleProceedings: 2019 International Conference on Computing, Electronics & Communications Engineering (iCCECE)
Book editorMiraz, M. H.
Excell, P. S.
Ware, A.
Soomro, S.
Ali, M.
ISBN978-1-7281-2138-3/19
Digital Object Identifier (DOI)https://doi.org/10.1109/iCCECE46942.2019.8941615
Web address (URL)https://doi.org/10.1109/iCCECE46942.2019.8941615
Copyright holder© 2019 IEEE.
Copyright informationPersonal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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