Exploring the application of artificial intelligence techniques in data analysis cancer detection: a systematic analysis

Tóm tắt

In the current context in the world in general and in Vietnam in particular, there are very few published articles and research on the use of Artificial Intelligence (AI) technology in early detection cancer. Therefore, this is the goal that I want to do this research through an extensive systematic study, through an extensive systematic study, this
 research investigates the AI utilization approaches in data analysis and diagnosis for cancer. AI has improved early detection, improved treatment strategies, and enhancing the prognosis for cancer patients, revolutionizing the field. The research looks at different AI models and evaluates how well they detect diseases such skin, lung, breast, and prostate cancers. These models include existing AI models, which have been developed by researchers, followed by Deep Learning (DL) models, Neural Network (NN), and hybrid methods. The literature review addresses the shortcomings of conventional techniques by describing the development of AI in healthcare and its crucial role in cancer detection and treatment. The methodology in this work outlines the systematic review process, including data sources, selection criteria, and analytical techniques. Results indicate that AI models significantly enhance the accuracy and speed of cancer detection, with CNNs excelling in image analysis and DL methods handling large datasets effectively. Hybrid models combine AI techniques to refine detection accuracy and efficiency. Despite these advancements, challenges like the requirement for extensive training data, high computational resources, and ethical concerns regarding patient data privacy persist. The report emphasizes the importance of standardized evaluation metrics to ensure the reliability and generalizability of AI applications in clinical settings. It concludes by discussing AI's potential to transform oncology, offering recommendations for future research to address current limitations. By integrating AI more effectively into clinical practice, significant advancements in early cancer detection and treatment can be achieved, ultimately improving patient care and outcomes.