Multi-head deep learning framework for pulmonary disease detection and severity scoring with modified progressive learning.

Autor: Khan AM; National University of Sciences and Technology, Islamabad, 44000, Pakistan., Akram MU; National University of Sciences and Technology, Islamabad, 44000, Pakistan., Nazir S; Department of Computing, Glasgow Caledonian University, Glasgow, UK., Hassan T; Center for Cyber-Physical Systems (C2PS), Department of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi 127788, United Arab Emirates., Khawaja SG; National University of Sciences and Technology, Islamabad, 44000, Pakistan., Fatima T; Resident Radiologist, Pakistan Institute of Medical Sciences, Islamabad, Pakistan.
Jazyk: angličtina
Zdroj: Biomedical signal processing and control [Biomed Signal Process Control] 2023 Aug; Vol. 85, pp. 104855. Date of Electronic Publication: 2023 Mar 24.
DOI: 10.1016/j.bspc.2023.104855
Abstrakt: Chest X-rays (CXR) are the most commonly used imaging methodology in radiology to diagnose pulmonary diseases with close to 2 billion CXRs taken every year. The recent upsurge of COVID-19 and its variants accompanied by pneumonia and tuberculosis can be fatal in some cases and lives could be saved through early detection and appropriate intervention for the advanced cases. Thus CXRs can be used for an automated severity grading of pulmonary diseases that can aid radiologists in making better and informed diagnoses. In this article, we propose a single framework for disease classification and severity scoring produced by segmenting the lungs into six regions. We present a modified progressive learning technique in which the amount of augmentations at each step is capped. Our base network in the framework is first trained using modified progressive learning and can then be tweaked for new data sets. Furthermore, the segmentation task makes use of an attention map generated within and by the network itself. This attention mechanism allows to achieve segmentation results that are on par with networks having an order of magnitude or more parameters. We also propose severity score grading for 4 thoracic diseases that can provide a single-digit score corresponding to the spread of opacity in different lung segments with the help of radiologists. The proposed framework is evaluated using the BRAX data set for segmentation and classification into six classes with severity grading for a subset of the classes. On the BRAX validation data set, we achieve F1 scores of 0.924 and 0.939 without and with fine-tuning, respectively. A mean matching score of 80.8% is obtained for severity score grading while an average area under receiver operating characteristic curve of 0.88 is achieved for classification.
Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(© 2023 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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