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Deep Learning Approaches for Automatic Localization in Medical Images

Alaskar, H, Hussain, A, Almaslukh, B, Vaiyapuri, T, Sbai, Z and Dubey, AK (2022) Deep Learning Approaches for Automatic Localization in Medical Images. Computational Intelligence and Neuroscience, 2022. p. 6347307. ISSN 1687-5265

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Recent revolutionary advances in deep learning (DL) have fueled several breakthrough achievements in various complicated computer vision tasks. The remarkable successes and achievements started in 2012 when deep learning neural networks (DNNs) outperformed the shallow machine learning models on a number of significant benchmarks. Significant advances were made in computer vision by conducting very complex image interpretation tasks with outstanding accuracy. These achievements have shown great promise in a wide variety of fields, especially in medical image analysis by creating opportunities to diagnose and treat diseases earlier. In recent years, the application of the DNN for object localization has gained the attention of researchers due to its success over conventional methods, especially in object localization. As this has become a very broad and rapidly growing field, this study presents a short review of DNN implementation for medical images and validates its efficacy on benchmarks. This study presents the first review that focuses on object localization using the DNN in medical images. The key aim of this study was to summarize the recent studies based on the DNN for medical image localization and to highlight the research gaps that can provide worthwhile ideas to shape future research related to object localization tasks. It starts with an overview on the importance of medical image analysis and existing technology in this space. The discussion then proceeds to the dominant DNN utilized in the current literature. Finally, we conclude by discussing the challenges associated with the application of the DNN for medical image localization which can drive further studies in identifying potential future developments in the relevant field of study.

Item Type: Article
Uncontrolled Keywords: Publications; Image Processing, Computer-Assisted; Machine Learning; Deep Learning; Neural Networks, Computer; Deep Learning; Image Processing, Computer-Assisted; Machine Learning; Neural Networks, Computer; Publications; 1109 Neurosciences; 1702 Cognitive Sciences; Neurology & Neurosurgery
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
T Technology > T Technology (General)
Divisions: Computer Science & Mathematics
Publisher: Hindawi Limited
SWORD Depositor: A Symplectic
Date Deposited: 16 Aug 2022 11:13
Last Modified: 16 Aug 2022 11:15
DOI or ID number: 10.1155/2022/6347307
URI: https://researchonline.ljmu.ac.uk/id/eprint/17394
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