Quantum machine learning is a rapidly emerging field that combines the principles of quantum computing with the techniques of machine learning. It uses quantum effects such as entanglement to improve the accuracy of traditional machine learning models. Quantum computing it is based on the ability of particles to exist in multiple states at the same time, allowing for the parallel processing of vast amounts of data. Quantum machine learning can be used to perform pattern recognition tasks, such as image classi ication and image segmentation, which are essential for image processing. By leveraging the power of quantum computing, quantum machine learning algorithms can process large amounts of data much more efficiently than traditional machine learning algorithms, resulting in faster and more accurate results. Additionally, quantum machine learning algorithms can improve the accuracy of image processing tasks by recognizing more subtle patterns in the data. This could potentially solve the medical image segmentation and classi ication to identify and predict cancers. This paper examines the intersection of quantum computing and machine learning models for medical image segmentation and the potential implications for advancing cancer research. The paper begins by describing the basics of quantum computing and machine learning algorithms, then goes on to explore how they can be combined to create powerful new systems for efficient cancer research. Subsequently the study presents image segmentation algorithms K-means and Q-means and further discusses the need for further research on the combination of quantum computing and machine learning, as well as potential ethical implications.
Select your language of interest to view the total content in your interested language