A Deep Learning Based Scalable and Adaptive Feature Extraction Framework for Medical Images

Loukil, Zainab ORCID: 0000-0003-2731-7051, Ali Mirza, Qublai Khan ORCID: 0000-0003-3403-2935, Sayers, William ORCID: 0000-0003-1677-4409 and Awan, Irfan (2024) A Deep Learning Based Scalable and Adaptive Feature Extraction Framework for Medical Images. Information Systems Frontiers, 26 (4). pp. 1279-1305. doi:10.1007/s10796-023-10391-9

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Abstract

Features extraction has a fundamental value in enhancing the scalability and adaptability n of medical image processing framework. The outcome of this stage has a tremendous effect on the reliability of the medical application being developed, particularly disease classification and prediction. The challenging side of features extraction frameworks, in relation to medical images, is influenced by the anatomical and morphological structure of the image which requires a powerful extraction system that highlights high- and low- level features. The complementary of both feature types reinforces the medical image content-based retrieval and allows to access visible structures as well as an in-depth understanding of related deep hidden components. Several existing techniques have been used towards extracting high- and low-level features separately, including Deep Learning based approaches. However, the fusion of these features remains a challenging task. Towards tackling the drawback caused by the lack of features combination and enhancing the reliability of features extraction methods, this paper proposes a new hybrid features extraction framework that focuses on the fusion and optimal selection of high- and low-level features. The scalability and reliability of the proposed method is achieved by the automated adjustment of the final optimal features based on real-time scenarios resulting an accurate and efficient medical images disease classification. The proposed framework has been tested on two different datasets to include BraTS and Retinal sets achieving an accuracy rate of 97% and 98.9%, respectively.

Item Type:	Article
Article Type:	Article
Uncontrolled Keywords:	Features extraction; Deep learning; High-level features; Low-level features; Optimal features fusion
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Divisions:	Schools and Research Institutes > School of Business, Computing and Social Sciences
Research Priority Areas:	Applied Business & Technology
Depositing User:	Susan Turner
Date Deposited:	26 Jul 2023 09:20
Last Modified:	03 Sep 2024 15:45
URI:	https://eprints.glos.ac.uk/id/eprint/12695

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