Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Review Article
Authors: Hussain, Dildara | Al-masni, Mohammed A.a | Aslam, Muhammada | Sadeghi-Niaraki, Abolghasemb | Hussain, Jamila | Gu, Yeong Hyeona; * | Naqvi, Rizwan Alic; *
Affiliations: [a] Department of Artificial Intelligence and Data Science, Sejong University, Seoul, Korea | [b] Department of Computer Science & Engineering and Convergence Engineering for Intelligent Drone, XR Research Center, Sejong University, Seoul, Korea | [c] Department of Intelligent Mechatronics Engineering, Sejong University, Seoul, Korea
Correspondence: [*] Corresponding authors. Yeong Hyeon Gu, Department of Artificial Intelligence and Data Science, Sejong University, Seoul 05006, Republic of Korea. E-mail: yhgu@sejong.ac.kr and Rizwan Ali Naqvi, Department of Intelligent Mechatronics Engineering, Sejong University, Seoul, Republic of Korea. E-mail: rizwanali@sejong.ac.kr.
Abstract: BACKGROUND: The emergence of deep learning (DL) techniques has revolutionized tumor detection and classification in medical imaging, with multimodal medical imaging (MMI) gaining recognition for its precision in diagnosis, treatment, and progression tracking. OBJECTIVE: This review comprehensively examines DL methods in transforming tumor detection and classification across MMI modalities, aiming to provide insights into advancements, limitations, and key challenges for further progress. METHODS: Systematic literature analysis identifies DL studies for tumor detection and classification, outlining methodologies including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their variants. Integration of multimodality imaging enhances accuracy and robustness. RESULTS: Recent advancements in DL-based MMI evaluation methods are surveyed, focusing on tumor detection and classification tasks. Various DL approaches, including CNNs, YOLO, Siamese Networks, Fusion-Based Models, Attention-Based Models, and Generative Adversarial Networks, are discussed with emphasis on PET-MRI, PET-CT, and SPECT-CT. FUTURE DIRECTIONS: The review outlines emerging trends and future directions in DL-based tumor analysis, aiming to guide researchers and clinicians toward more effective diagnosis and prognosis. Continued innovation and collaboration are stressed in this rapidly evolving domain. CONCLUSION: Conclusions drawn from literature analysis underscore the efficacy of DL approaches in tumor detection and classification, highlighting their potential to address challenges in MMI analysis and their implications for clinical practice.
Keywords: Multimodal medical image, deep learning, MRI, CT, PET, fusion, segmentation, image analysis, deep learning, GANs
DOI: 10.3233/XST-230429
Journal: Journal of X-Ray Science and Technology, vol. 32, no. 4, pp. 857-911, 2024
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
sales@iospress.com
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
info@iospress.nl
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office info@iospress.nl
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
china@iospress.cn
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
如果您在出版方面需要帮助或有任何建, 件至: editorial@iospress.nl