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Shen, L. (2023). Retracted: Implementation of CT image segmentation based on an image segmentation algorithm. Applied Bionics and Biomechanics, 2023, 9840516. https://doi.org/10.1155/2022/2047537Search in Google Scholar
Glorindal, G., Mozhiselvi, S. A., Kumar, T. A., Kumaran, K., Katema, P. C., Kandimba, T. (2021). A simplified approach for melanoma skin disease identification. In 2021 International Conference on System, Computation, Automation and Networking (ICSCAN). IEEE. https://doi.org/10.1109/ICSCAN53069.2021.9526511Search in Google Scholar
Chai, R. (2021). Otsu’s image segmentation algorithm with memory-based fruit fly optimization algorithm. Complexity, 2021, 5564690. https://doi.org/10.1155/2021/5564690Search in Google Scholar
Li, M., Sha, H., Liu, H. (2022). Microfeature segmentation algorithm for biological images using improved density peak clustering. Computational and Mathematical Methods in Medicine, 2022, 8630449. https://doi.org/10.1155/2022/8630449Search in Google Scholar
Zhang, Y., Balochian, S., Agarwal, P., Bhatnagar, V., Housheya, O. J. (2014). Artificial intelligence and its applications. Mathematical Problems in Engineering, 2014, 840491. https://doi.org/10.1155/2014/840491Search in Google Scholar
Chen, W., Yu, C., Tu, C., Lyu, Z., Tang, J., Ou, S., Fu, Y., Xue, Z. (2020). A survey on hand pose estimation with wearable sensors and computer-vision-based methods. Sensors, 20 (4), 1074. https://doi.org/10.3390/s20041074Search in Google Scholar
Song, Y., Cisternino, F., Mekke, J. M., de Borst, G. J., de Kleijn, D. P. V., Pasterkamp, G., Vink, A., Glastonbury, C. A., van der Laan, S. W., Miller, C. L. (2023). An automatic entropy method to efficiently mask histology whole-slide images. Scientific Reports, 13 (1), 4321. https://doi.org/10.1038/s41598-023-29638-1Search in Google Scholar
Buyck, F., Vandemeulebroucke, J., Ceranka, J., Van Gestel, F., Cornelius, J. F., Duerinck, J., Bruneau, M. (2023). Computer-vision based analysis of the neurosurgical scene - A systematic review. Brain and Spine, 3, 102706. https://doi.org/10.1016/j.bas.2023.102706Search in Google Scholar
Putra, R. H., Doi, C., Yoda, N., Astuti, E. R., Sasaki, K. (2022). Current applications and development of artificial intelligence for digital dental radiography. Dentomaxillofacial Radiology, 51 (1), 20210197. https://doi.org/10.1259/dmfr.20210197Search in Google Scholar
Froese, L., Dian, J., Batson, C., Gomez, A., Sainbhi, A. S., Unger, B., Zeiler, F. A. (2021). Computer vision for continuous bedside pharmacological data extraction: A novel application of artificial intelligence for clinical data recording and biomedical research. Frontiers in Big Data, 4, 689358. https://doi.org/10.3389/fdata.2021.689358Search in Google Scholar
Parameswari, A., Bhavani, S., Kumar, K. V. (2024). A deep learning based glioma tumour detection using efficient visual geometry group convolutional neural networks architecture. Brazilian Archives of Biology and Technology, 67, e24230705. https://doi.org/10.1590/1678-4324-2024230705Search in Google Scholar
Liyanage, H., Liaw, S.-T., Jonnagaddala, J., Schreiber, R., Kuziemsky, C., Terry, A. L., de Lusignan, S. (2019). Artificial intelligence in primary health care: Perceptions, issues, and challenges. Yearbook of Medical Informatics, 28 (1), 41-46. https://doi.org/10.1055/s-0039-1677901Search in Google Scholar
Parameswari, A., Bhavani, S., Kumar, K. V. (2023). A convolutional deep neural network based brain tumor diagnoses using clustered image and feature-supported classifier (CIFC) technique. Brazilian Archives of Biology and Technology, 66, e23230012. http://dx.doi.org/10.1590/1678-4324-2023230012Search in Google Scholar
Parameswari, A., Kumar, K. V., Gopinath, S. (2022). Thermal analysis of Alzheimer’s disease prediction using random forest classification model. Materials Today: Proceedings, 66 (3), 815-821. https://doi.org/10.1016/j.matpr.2022.04.357Search in Google Scholar
Stephe, S., Jayasankar, T., Kumar, K. V. (2019). Motor imagery recognition of EEG signal using cuckoo-search masking empirical mode decomposition. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8 (11), 2717-2720. http://dx.doi.org/10.35940/ijitee.K2175.0981119Search in Google Scholar