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- W3216179994 abstract "The field of neural networks has seen significant advances in recent years with the development of deep and convolutional neural networks. Although many of the current works address real-valued models, recent studies reveal that neural networks with hypercomplex-valued parameters can better capture, generalize, and represent the complexity of multidimensional data. This paper explores the quaternion-valued convolutional neural network application for a pattern recognition task from medicine, namely, the diagnosis of acute lymphoblastic leukemia. Precisely, we compare the performance of real-valued and quaternion-valued convolutional neural networks to classify lymphoblasts from the peripheral blood smear microscopic images. The quaternion-valued convolutional neural network achieved better or similar performance than its corresponding real-valued network but using only 34% of its parameters. This result confirms that quaternion algebra allows capturing and extracting information from a color image with fewer parameters." @default.
- W3216179994 created "2021-12-06" @default.
- W3216179994 creator A5012009095 @default.
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- W3216179994 date "2021-01-01" @default.
- W3216179994 modified "2023-10-17" @default.
- W3216179994 title "Quaternion-Valued Convolutional Neural Network Applied for Acute Lymphoblastic Leukemia Diagnosis" @default.
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- W3216179994 doi "https://doi.org/10.1007/978-3-030-91699-2_20" @default.
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