Case Study of Next-Generation Artificial Intelligence in Medical Image Diagnosis Based on Cloud Computing

Authors

  • Jiang Wu Computer Science,University of Southern California,Los Angeles, CA, USA
  • Hongbo Wang Computer Science, University of Southern California,Los Angeles, CA
  • Chunhe Ni Computer Science,University of Texas at Dallas,Richardson, TX, USA
  • Chenwei Zhang Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL,USA
  • Wenran Lu Electrical Engineering, University of Texas at Austin, Austin, TX,USA

DOI:

https://doi.org/10.53469/jtpes.2024.04(02).10

Keywords:

Cloud computing, Next generation intelligence, Medical imaging, Diagnostic detection

Abstract

Cloud computing technology is a computing model based on the Internet, which can provide elastic, scalable, reliable and secure computing, storage and network resources to meet the various computing needs of enterprises and individuals. Cloud computing technology has become an important driving force for enterprise digital transformation and innovation. Through cloud computing technology, enterprises can run their business more efficiently, reduce costs, increase productivity, protect data and systems from attacks and leaks, keep systems always available, open new markets, and more. At the same time, cloud computing technology has also brought more innovation opportunities, such as artificial intelligence, big data and the Internet of Things, so as to help enterprises better meet customer needs and explore new markets. In addition, cloud computing combined with artificial intelligence has brought a lot of convenience to many traditional industries, such as medical care, education, and scientific research. By lowering barriers to entry, cloud computing levels the playing field and makes it easier for these businesses to compete with established players. With the wide application of technologies such as 5G, artificial intelligence, big data and the Internet of Things, cloud computing infrastructure will become more popular and powerful, but also face more challenges and opportunities. We need to constantly pay attention to the evolution trend of cloud computing and actively respond to new challenges and opportunities, so as to better use cloud computing technology to promote the development and innovation of enterprises and achieve sustainable development. In this paper, by analyzing the practical application cases of the next generation intelligence combined with cloud computing to the medical image analysis and diagnosis industry, the future prospects of cloud computing and next generation artificial intelligence are expounded.

References

“Based on Intelligent Advertising Recommendation and Abnormal Advertising Monitoring System in the Field of Machine Learning”. International Journal of Computer Science and Information Technology, vol. 1, no. 1, Dec. 2023, pp. 17-23, https://doi.org/10.62051/ijcsit.v1n1.03.

Pan, Yiming, et al. “Application of Three-Dimensional Coding Network in Screening and Diagnosis of Cervical Precancerous Lesions”. Frontiers in Computing and Intelligent Systems, vol. 6, no. 3, Jan. 2024, pp. 61-64, https://doi.org/10.54097/mi3VM0yB.

Tan, Kai, et al. “Integrating Advanced Computer Vision and AI Algorithms for Autonomous Driving Systems”. Journal of Theory and Practice of Engineering Science, vol. 4, no. 01, Jan. 2024, pp. 41-48, doi:10.53469/jtpes.2024.04(01).06.

K. Jin, Z. Z. Zhong and E. Y. Zhao, "Sustainable Digital Marketing Under Big Data: An AI Random Forest Model Approach," in IEEE Transactions on Engineering Management, vol. 71, pp. 3566-3579, 2024, doi: 10.1109/TEM.2023.3348991.

Chen, Wangmei, et al. “Applying Machine Learning Algorithm to Optimize Personalized Education Recommendation System”. Journal of Theory and Practice of Engineering Science, vol. 4, no. 01, Feb. 2024, pp. 101-8, doi:10.53469/jtpes.2024.04(01).14.

“Implementation of Computer Vision Technology Based on Artificial Intelligence for Medical Image Analysis”. International Journal of Computer Science and Information Technology, vol. 1, no. 1, Dec. 2023, pp. 69-76, https://doi.org/10.62051/ijcsit.v1n1.10.

Dong, Xinqi, et al. “The Prediction Trend of Enterprise Financial Risk Based on Machine Learning ARIMA Model”. Journal of Theory and Practice of Engineering Science, vol. 4, no. 01, Jan. 2024, pp. 65-71, doi:10.53469/jtpes.2024.04(01).09.

“A Deep Learning-Based Algorithm for Crop Disease Identification Positioning Using Computer Vision”. International Journal of Computer Science and Information Technology, vol. 1, no. 1, Dec. 2023, pp. 85-92, https://doi.org/10.62051/ijcsit.v1n1.12.

Du, S., Li, L., Wang, Y., Liu, Y., & Pan, Y. (2023). Application of HPV-16 in Liquid-Based thin Layer Cytology of Host Genetic Lesions Based on AI Diagnostic Technology Presentation of Liquid. Journal of Theory and Practice of Engineering Science, 3(12), 1-6.

Xin, Q., He, Y., Pan, Y., Wang, Y., & Du, S. (2023). The implementation of an AI-driven advertising push system based on a NLP algorithm. International Journal of Computer Science and Information Technology, 1(1), 30-37.

He, Yuhang, et al. “Intelligent Fault Analysis With AIOps Technology”. Journal of Theory and Practice of Engineering Science, vol. 4, no. 01, Feb. 2024, pp. 94-100, doi:10.53469/jtpes.2024.04(01).13.

Chen , J., Xiong, J., Wang, Y., Xin, Q., & Zhou, H. (2024). Implementation of an AI-based MRD Evaluation and Prediction Model for Multiple Myeloma. Frontiers in Computing and Intelligent Systems, 6(3), 127-131. https://doi.org/10.54097/zJ4MnbWW

Tan, Kai, et al. “Integrating Advanced Computer Vision and AI Algorithms for Autonomous Driving Systems”. Journal of Theory and Practice of Engineering Science, vol. 4, no. 01, Jan. 2024, pp. 41-48, doi:10.53469/jtpes.2024.04(01).06.

“Exploring New Frontiers of Deep Learning in Legal Practice: A Case Study of Large Language Models”. International Journal of Computer Science and Information Technology, vol. 1, no. 1, Dec. 2023, pp. 131-8, https://doi.org/10.62051/ijcsit.v1n1.18.

Development of Machine Learning and Artificial Intelligence in Toxic Pathology. (2024). Frontiers in Computing and Intelligent Systems, 6(3), 137-141. https://doi.org/10.54097/Be1ExjZa

Jili Qian, et al. “Analysis and Diagnosis of Hemolytic Specimens by AU5800 Biochemical Analyzer Combined With AI Technology”. Frontiers in Computing and Intelligent Systems, vol. 6, no. 3, Jan. 2024, pp. 100-3, https://doi.org/10.54097/qoseeQ5N.

“A Deep Learning-Based Algorithm for Crop Disease Identification Positioning Using Computer Vision”. International Journal of Computer Science and Information Technology, vol. 1, no. 1, Dec. 2023, pp. 85-92, https://doi.org/10.62051/ijcsit.v1n1.12.

Pan, Yiming, et al. “Application of Three-Dimensional Coding Network in Screening and Diagnosis of Cervical Precancerous Lesions”. Frontiers in Computing and Intelligent Systems, vol. 6, no. 3, Jan. 2024, pp. 61-64, https://doi.org/10.54097/mi3VM0yB.

Wei, Kuo, et al. “Strategic Application of AI Intelligent Algorithm in Network Threat Detection and Defense”. Journal of Theory and Practice of Engineering Science, vol. 4, no. 01, Jan. 2024, pp. 49-57, doi:10.53469/jtpes.2024.04(01).07.

“Unveiling the Future Navigating Next-Generation AI Frontiers and Innovations in Application”. International Journal of Computer Science and Information Technology, vol. 1, no. 1, Dec. 2023, pp. 147-56, https://doi.org/10.62051/ijcsit.v1n1.20.

Zong, Yanqi, et al. “Improvements and Challenges in StarCraft II Macro-Management A Study on the MSC Dataset”. Journal of Theory and Practice of Engineering Science, vol. 3, no. 12, Dec. 2023, pp. 29-35, doi:10.53469/jtpes.2023.03(12).05.

An Overview of the Development of Stereotactic Body Radiation Therapy. (2024). Frontiers in Computing and Intelligent Systems, 6(3), 56-60. https://doi.org/10.54097/09nIy12x.

Zheng, Jiajian, et al. “The Credit Card Anti-Fraud Detection Model in the Context of Dynamic Integration Selection Algorithm”. Frontiers in Computing and Intelligent Systems, vol. 6, no. 3, Jan. 2024, pp. 119-22, https://doi.org/10.54097/a5jafgdv.

Wang, Sihao, et al. “Diabetes Risk Analysis Based on Machine Learning LASSO Regression Model”. Journal of Theory and Practice of Engineering Science, vol. 4, no. 01, Jan. 2024, pp. 58-64, doi:10.53469/jtpes.2024.04(01).08.

“Enhancing Computer Digital Signal Processing through the Utilization of RNN Sequence Algorithms”. International Journal of Computer Science and Information Technology, vol. 1, no. 1, Dec. 2023, pp. 60-68, https://doi.org/10.62051/ijcsit.v1n1.09.

Yu, L., Liu, B., Lin, Q., Zhao, X., & Che, C. (2024). Semantic Similarity Matching for Patent Documents Using Ensemble BERT-related Model and Novel Text Processing Method. arXiv preprint arXiv:2401.06782.

Huang, J., Zhao, X., Che, C., Lin, Q., & Liu, B. (2024). Enhancing Essay Scoring with Adversarial Weights Perturbation and Metric-specific AttentionPooling. arXiv preprint arXiv:2401.05433.

Du, Shuqian, et al. “Application of HPV-16 in Liquid-Based Thin Layer Cytology of Host Genetic Lesions Based on AI Diagnostic Technology Presentation of Liquid”. Journal of Theory and Practice of Engineering Science, vol. 3, no. 12, Dec. 2023, pp. 1-6, doi:10.53469/jtpes.2023.03(12).01.

Pan, Yiming, et al. “Application of Three-Dimensional Coding Network in Screening and Diagnosis of Cervical Precancerous Lesions”. Frontiers in Computing and Intelligent Systems, vol. 6, no. 3, Jan. 2024, pp. 61-64, https://doi.org/10.54097/mi3VM0yB.

Downloads

Published

2024-03-01

How to Cite

Wu, J., Wang, H., Ni, C., Zhang, C., & Lu, W. (2024). Case Study of Next-Generation Artificial Intelligence in Medical Image Diagnosis Based on Cloud Computing. Journal of Theory and Practice of Engineering Science, 4(02), 66–73. https://doi.org/10.53469/jtpes.2024.04(02).10