This work is licensed under the Creative Commons Attribution 4.0 International License.
Zhou, T., Lu, H., Hu, F., & Wang, H. (2021). A new robust adaptive fusion method for double-modality medical image pet/ct. BioMed Research International.Search in Google Scholar
Fatah, N., Ulwali, R. A., Alwally, H. A., & Physics, R. (2021). Ct&pet medical image fusion techniques based on statistical criteria. Solid State Technology.Search in Google Scholar
Dou, W. W., & Ji, Y. (2021). External financing and customer capital: a financial theory of markups. Management Science, 67(9), -.Search in Google Scholar
Abdulwahid, M. M., & Basil, N. (2021). Review on chaotic theory using dna encoding with image encryption. Informatica, 2(1), 14-19.Search in Google Scholar
Huang, X., Wei, Z., & Wen, F. (2021). Image processing technology in high frequency vibration direction and amplitude measurement. Journal of Intelligent and Fuzzy Systems(2), 1-8.Search in Google Scholar
Weiller, C., Reisert, M., Glauche, V., Musso, M., & Rijntjes, M. (2022). The dual-loop model for combining external and internalworlds in our brain. NeuroImage, 263, 119583.Search in Google Scholar
Wei, C., Chen, R., Xun, M., & Xin, Q. (2019). A novel fpga-based real-time 3d shape reconstruction system with potential applications in medical image processing and analysis. Basic & clinical pharmacology & toxicology.(S1), 125.Search in Google Scholar
Shao, Y., & Wang, S. (2019). Medical image processing and analysis method based on fuzzy mathematics. Basic & clinical pharmacology & toxicology.(S2), 125.Search in Google Scholar
Franchini, S., Gentile, A., Sorbello, F., Vassallo, G., & Vitabile, S. (2015). Conformalalu: a conformal geometric algebra coprocessor for medical image processing. Computers IEEE Transactions on, 64(4), 955-970.Search in Google Scholar
Krefting, D., Vossberg, M., & Tolxdorff, T. (2013). Simplified grid implementation of medical image processing algorithms using a workflow managment system. Clinical Anatomy, 26, 242-246.Search in Google Scholar
Zhang, X., Chen, Z., Gao, J., Huang, W., Li, P., & Zhang, J. (2022). A two-stage deep transfer learning model and its application for medical image processing in traditional chinese medicine. Knowledge-based systems(Mar.5), 239.Search in Google Scholar
Gulati, K. (2022). A novel machine-learning-based hybrid CNN model for tumor identification in medical image processing. Sustainability, 14.Search in Google Scholar
Gong, Y. Z. Y. (2021). Generative adversarial networks in medical image processing. Current pharmaceutical design, 27(15).Search in Google Scholar
aban ztürk a, B, R. A., & C, N. A. (2020). Variants of artificial bee colony algorithm and its applications in medical image processing. Applied Soft Computing, 97.Search in Google Scholar
Toshiba, K. K. (2017). Ultrasonic diagnostic apparatus, medical image processing apparatus, and medical image processing method. Journal of the Acoustical Society of America, 130(2), 1088.Search in Google Scholar
Sunitha, C., & Sathya, S. (2013). Analysis and visualization of medical image processing. Orvosi Hetilap, 105, 258-259.Search in Google Scholar
Yang, R., Yu, J., Yin, J., Liu, K., & Xu, S. (2022). A dense r-CNN multi-target instance segmentation model and its application in medical image processing. IET image processing(9), 16.Search in Google Scholar
An‐Di Yim, Corron, L., & Stull, K. (2021). A new tool for medical image processing in biological anthropology and anatomy research. The FASEB Journal, 35(S1), -.Search in Google Scholar
Alnazer, I., Bourdon, P., Urruty, T., Falou, O., & Fernandez-Maloigne, C. (2021). Recent advances in medical image processing for the evaluation of chronic kidney disease. Medical Image Analysis, 101960.Search in Google Scholar
Zhu, S., Wang, L., & Duan, S. (2016). Memristive pulse coupled neural network with applications in medical image processing. Neurocomputing, S0925231216313820.Search in Google Scholar
Song, T., Yu, X., Yu, S., Ren, Z., & Qu, Y. (2021). Feature extraction processing method of medical image fusion based on neural network algorithm. Complexity.Search in Google Scholar
Wang, L., Meng, Z., Sun, Y., Guo, L., & Zhou, M. (2015). Design and analysis of a novel chaotic diagonal recurrent neural network. Communications in Nonlinear Science & Numerical Simulation, 26(1-3), 11-23.Search in Google Scholar
Laje, Rodrigo, Buonomano, Dean, & V. (2013). Robust timing and motor patterns by taming chaos in recurrent neural networks. Nature Neuroscience.Search in Google Scholar
Zhang, B. Z. Y. (2017). The performance evaluation of diagonal recurrent neural network with different chaos neurons. Neural computing & applications, 28(7).Search in Google Scholar
Saeed, A., Li, C., Gan, Z., Xie, Y., & Liu, F. (2022). A simple approach for short-term wind speed interval prediction based on independently recurrent neural networks and error probability distribution. Energy, 238.Search in Google Scholar
Took, C. C., & Mandic, D. (2022). Weight sharing for lms algorithms: convolutional neural networks inspired multichannel adaptive filtering. Digital Signal Processing.Search in Google Scholar
Bredikhin, A. I. (2019). Training algorithms for convolutional neural networks. Yugra State University Bulletin, 15(1), 41-54.Search in Google Scholar
Dhou, K., & Cruzen, C. (2022). A creative chain coding technique for bi-level image compression inspired by the netlogo hiv agent-based modeling simulation. Journal of computational science(May), 61.Search in Google Scholar
Sekar, R., & Ravi, G. (2022). Taylor sun flower optimization-based compressive sensing for image compression and recovery. The Computer Journal.Search in Google Scholar
Luo, S., Fang, G., & Song, M. (2023). Deep semantic image compression via cooperative network pruning. Journal of Visual Communication and Image Representation.Search in Google Scholar
Szajna, A., Kostrzewski, M., Ciebiera, K., Stryjski, R., & Sciubba, E. (2021). Application of the deep cnn-based method in industrial system for wire marking identification. Energies(12).Search in Google Scholar
Cai, R., Li, J., Li, G., Tang, D., & Tan, Y. (2021). A review of the application of cnn-based computer vision in civil infrastructure maintenance. Springer Books.Search in Google Scholar