Application of Machine Learning Algorithms for High-Accuracy Image Segmentation in Medical Imaging
DOI:
https://doi.org/10.62951/ijeemcs.v1i1.72Keywords:
Machine learning, image segmentation, medical imaging, U-Net, Fully Convolutional Networks, Mask R-CNN, diagnosis accuracyAbstract
Accurate image segmentation is a pivotal process in medical imaging, essential for supporting diagnosis, treatment planning, and monitoring disease progression. This study evaluates the effectiveness of machine learning algorithms, including U-Net, Fully Convolutional Networks (FCNs), and Mask R-CNN, in achieving high-precision segmentation of medical images. Experimental results demonstrate that these models significantly enhance segmentation accuracy, enabling more precise diagnostic outcomes in clinical settings and advancing the development of automated medical imaging technologies.
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