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Part of the book series: Studies in Computational Intelligence ((SCI,volume 862))

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Abstract

Liver tumor segmentation from computed tomography images is an essential task for the automated diagnosis and treatment of liver cancer. However, such task is di cult due to the variability of morphologies, di use boundaries, heterogeneous densities, and sizes of the lesions. In this work we develop a new system designed for the segmentation of tumors from images acquired by computed tomography, the proposed system uses a network based on convolutional neural networks (CNN). The results are compared with a segmentation carried out by medical experts.

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Correspondence to Alberto Ochoa .

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Mejía, J., Ochoa, A., Mederos, B. (2020). Improving Segmentation of Liver Tumors Using Deep Learning. In: Castillo, O., Melin, P., Kacprzyk, J. (eds) Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications. Studies in Computational Intelligence, vol 862. Springer, Cham. https://doi.org/10.1007/978-3-030-35445-9_52

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