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A cognitive deep learning approach for medical image processing
University of Petra, Jordan.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0002-6309-2892
University of Tartu, Estonia.
Malmö University.
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2024 (English)In: Scientific Reports, E-ISSN 2045-2322, Vol. 14, no 1, article id 4539Article in journal (Refereed) Published
Abstract [en]

In ophthalmic diagnostics, achieving precise segmentation of retinal blood vessels is a critical yet challenging task, primarily due to the complex nature of retinal images. The intricacies of these images often hinder the accuracy and efficiency of segmentation processes. To overcome these challenges, we introduce the cognitive DL retinal blood vessel segmentation (CoDLRBVS), a novel hybrid model that synergistically combines the deep learning capabilities of the U-Net architecture with a suite of advanced image processing techniques. This model uniquely integrates a preprocessing phase using a matched filter (MF) for feature enhancement and a post-processing phase employing morphological techniques (MT) for refining the segmentation output. Also, the model incorporates multi-scale line detection and scale space methods to enhance its segmentation capabilities. Hence, CoDLRBVS leverages the strengths of these combined approaches within the cognitive computing framework, endowing the system with human-like adaptability and reasoning. This strategic integration enables the model to emphasize blood vessels, accurately segment effectively, and proficiently detect vessels of varying sizes. CoDLRBVS achieves a notable mean accuracy of 96.7%, precision of 96.9%, sensitivity of 99.3%, and specificity of 80.4% across all of the studied datasets, including DRIVE, STARE, HRF, retinal blood vessel and Chase-DB1. CoDLRBVS has been compared with different models, and the resulting metrics surpass the compared models and establish a new benchmark in retinal vessel segmentation. The success of CoDLRBVS underscores its significant potential in advancing medical image processing, particularly in the realm of retinal blood vessel segmentation. © 2024. The Author(s).

Place, publisher, year, edition, pages
Springer Nature, 2024. Vol. 14, no 1, article id 4539
Keywords [en]
Algorithms, Cognition, Deep Learning, Fundus Oculi, Humans, Image Processing, Computer-Assisted, Retinal Vessels, article, benchmarking, blood vessel, diagnosis, human, image processing, major clinical study, retina blood vessel, retina image
National Category
Medical Imaging
Identifiers
URN: urn:nbn:se:bth-26041DOI: 10.1038/s41598-024-55061-1ISI: 001177317400011PubMedID: 38402321Scopus ID: 2-s2.0-85186271613OAI: oai:DiVA.org:bth-26041DiVA, id: diva2:1843230
Available from: 2024-03-08 Created: 2024-03-08 Last updated: 2025-02-09Bibliographically approved

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Alawadi, Sadi

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