<![CDATA[Medical image processing is a crucial field in healthcare, especially in supporting diagnosis and clinical decision-making. Artificial Neural Networks (ANNs) have become an effective tool in medical image processing, but challenges in ANN optimization still need to be addressed to achieve higher accuracy and better efficiency. This article examines JST optimization methods applied to medical image processing. Various techniques such as network architecture adjustment, regulation, and the use of advanced optimization algorithms are explored in this study. The results show that JST optimization can significantly improve the performance in pattern recognition and classification of medical images.]]>
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