1. Liu, F., et al., Deep convolutional neural network and 3D deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic resonance in medicine, 2018. 79(4): p. 2379-2391. 2. Aljabri, M. and M. AlGhamdi, A review on the use of deep learning for medical images segmentation. Neurocomputing, 2022. 3. Zhou, Z., et al., Deep convolutional neural network for segmentation of knee joint anatomy. Magnetic resonance in medicine, 2018. 80(6): p. 2759-2770. 4. Liu, X., et al., A review of deep-learning-based medical image segmentation methods. Sustainability, 2021. 13(3): p. 1224. 5. Liu, X., et al., A review of deep-learning-based medical image segmentation methods. Sustainability, 2021. 13(3): p. 1224. 6. Khan, M.Z., et al., Deep neural architectures for medical image semantic segmentation. IEEE Access, 2021. 9: p. 83002-83024. 7. Loghmani, N., Shirmohammadi, M. M., & Chahardoli, M. (2018). Face recognition using the LCS algorithm. Revista Publicando, *5*(14), 1–23. [ Article] [ Google Scholar] 8. Chen, H., et al., Brain tumor segmentation with deep convolutional symmetric neural network. Neurocomputing, 2020. 392: p. 305-313. 9. M. M. Shirmohammadi, M. Chahardoli, and H. Zargari, Introduction to Multimedia. Hamadan: Daneshjo, 2011. ISBN: 971-964-543-104-2. [ Google Scholar] 10. Gohariyan, E., Esmaeilpour, M., & Shirmohammadi, M. M. (2016). The Combination of Mammography and MRI for Diagnosing Breast Cancer Using Fuzzy NN and SVM. International Journal of Interactive Multimedia and Artificial Intelligence, 4(5), 20–. https://doi.org/10.9781/ijimai.2017.453 [ Google Scholar] 11. Göçeri, E. Impact of deep learning and smartphone technologies in dermatology: Automated diagnosis. in 2020 tenth international conference on image processing theory, tools and applications (IPTA). 2020. IEEE. 12. Nadeem, M.W., et al., Bone age assessment empowered with deep learning: a survey, open research challenges and future directions. Diagnostics, 2020. 10(10): p. 781. 13. Simi Margarat, G., et al., Early diagnosis of tuberculosis using deep learning approach for iot based healthcare applications. Computational Intelligence and Neuroscience, 2022. 2022. 14. Havaei, M., et al., Deep learning trends for focal brain pathology segmentation in MRI. Machine learning for health informatics: state-of-the-art and future challenges, 2016: p. 125-148. 15. Hashemi, S.R., et al., Asymmetric loss functions and deep densely-connected networks for highly-imbalanced medical image segmentation: Application to multiple sclerosis lesion detection. IEEE Access, 2018. 7: p. 1721-1735. 16. Shirmohammadi, M. M. (2025). AI unboxed: Tools & techniques for the future (Vol. 1). ResearchGate. https://www.researchgate.net/publication/389659219_AI_UNBOXED_TOOLS_TECHNIQUES_FOR_THE_FUTURE [ Article] [ Google Scholar] 17. Karimi, I., Esmaeilpour, M., & ShirMohammadi, M. M. (2020). A novel algorithm to enhance the SURF method for measuring similarity of 2D images. Applied Research in Engineering and Technology, 18(3), 77–94. [ Google Scholar] 18. Li, J., et al., Automatic fetal head circumference measurement in ultrasound using random forest and fast ellipse fitting. IEEE journal of biomedical and health informatics, 2017. 22(1): p. 215-223. 19. Esmaeilpour, M., & ShirMohammadi, M. M. (2019). A novel method for brain tumor detection in MRI images (Report No. 2019). Islamic Azad University, Hamedan Branch. [ Google Scholar] 20. Asgari, T., & ShirMohammadi, M. M. (2018). Review of medical and dental images using image processing techniques. In Proceedings of the 3rd International Conference on Applied Research in Science and Engineering (Vol. 3). [ Google Scholar] 21. Lv, Z., et al., Deep learning-based smart predictive evaluation for interactive multimedia-enabled smart healthcare. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 2022. 18(1s): p. 1-20. 22. Rafique, A.A., A. Jalal, and K. Kim, Automated sustainable multi-object segmentation and recognition via modified sampling consensus and kernel sliding perceptron. Symmetry, 2020. 12(11): p. 1928. 23. Shi, F., et al., Review of artificial intelligence techniques in imaging data acquisition, segmentation, and diagnosis for COVID-19. IEEE reviews in biomedical engineering, 2020. 14: p. 4-15. 24. Yahyatabar, M., P. Jouvet, and F. Cheriet. Dense-Unet: a light model for lung fields segmentation in Chest X-Ray images. in 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 2020. IEEE. 25. Deniz, C.M., et al., Segmentation of the proximal femur from MR images using deep convolutional neural networks. Scientific reports, 2018. 8(1): p. 16485. 26. Dargan, S., et al., A survey of deep learning and its applications: a new paradigm to machine learning. Archives of Computational Methods in Engineering, 2020. 27: p. 1071-1092. 27. Chahhardoli, M., ShirMohammadi, M. M., & Shabani, R. (2005). A revolution in telemedicine services: Utilizing wireless sensor networks to assist patients. In Proceedings of the First National Conference on the Application of Information Technology in Medical Sciences (Vol. 1). Shahid Beheshti University. [ Google Scholar] 28. Haskins, G., U. Kruger, and P. Yan, Deep learning in medical image registration: a survey. Machine Vision and Applications, 2020. 31: p. 1-18. 29. Neelakandan, S., et al., Blockchain with deep learning-enabled secure healthcare data transmission and diagnostic model. International Journal of Modeling, Simulation, and Scientific Computing, 2022. 13(04): p. 2241006. 30. Dey, N., et al., Social group optimization supported segmentation and evaluation of skin melanoma images. Symmetry, 2018. 10(2): p. 51.
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