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A cognitive deep learning approach for medical image processing
Department of Data Science and Artificial Intelligence, The University of Petra, Amman, Jordanc.
Department of Computer Science, Blekinge Institute of Technology, Karlskrona, Sweden; Computer Graphics and Data Engineering (COGRADE) Research Group, University of Santiago de Compostela, Santiago de Compostela, Spain.
Institute of Computer Science, Delta Research Centre, University of Tartu, Tartu, Estonia.
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).ORCID iD: 0000-0002-8025-4734
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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.

Place, publisher, year, edition, pages
Springer Nature, 2024. Vol. 14, no 1, article id 4539
Keywords [en]
Algorithms, Cognition, Deep Learning, Fundus Oculi, Humans, Computer-Assisted Image Processing/methods, Retinal Vessels/diagnostic imaging
National Category
Medical Image Processing Computer graphics and computer vision
Identifiers
URN: urn:nbn:se:mau:diva-66272DOI: 10.1038/s41598-024-55061-1ISI: 001177317400011PubMedID: 38402321Scopus ID: 2-s2.0-85186271613OAI: oai:DiVA.org:mau-66272DiVA, id: diva2:1843145
Available from: 2024-03-08 Created: 2024-03-08 Last updated: 2025-02-01Bibliographically approved

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Alawadi, SadiAlkhabbas, Fahed

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