<![CDATA[The background of this research focuses on the recognition of medical image patterns for disease detection using artificial intelligence technology. Although Convolutional Neural Networks (CNNs) have been widely used, the models are limited in terms of accuracy and efficiency in processing complex medical images. Quantum Neural Networks (QNNs) are considered as a potential solution to address this problem, by leveraging quantum computing to improve speed and accuracy. The purpose of this study is to explore the application of QNN in the recognition of medical image patterns, as well as to compare its performance with more conventional CNN models. The study used a dataset of medical images from cancer and heart disease, which were divided into training and testing data. QNN and CNN were tested on the same dataset to compare accuracy, speed, and efficiency. The results showed that QNN produced 92% accuracy in breast cancer detection, higher than CNN which only reached 88%. QNN is also more efficient in terms of processing speed, with lower use of computing resources. The conclusion of this study shows that QNN has great potential to be used in the recognition of medical image patterns, with significant advantages in terms of accuracy and efficiency. This research paves the way for the further development of QNN technology in medical applications and disease diagnosis.]]>
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