Quaternion-Valued Convolutional Neural Network Applied for Acute Lymphoblastic Leukemia Diagnosis

• Main Authors: Granero, Marco Aurélio, Hernández, Cristhian Xavier, Valle, Marcos Eduardo
• Format: Journal Article
• Language: English
• Published: 13-12-2021
• Subjects: Computer Science - Computer Vision and Pattern Recognition; Computer Science - Learning; Computer Science - Neural and Evolutionary Computing
• DOI: 10.48550/arxiv.2112.06685

A. Britto and K. Valdivia Delgado (Eds.): BRACIS 2021, LNAI 13074,
pp. 280-293, 2021. Springer Nature Switzerland AG 2021 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.