Quaternion-Valued Convolutional Neural Network Applied for Acute Lymphoblastic Leukemia Diagnosis
This work addresses a domain-specific medical diagnosis problem, offering a parameter-efficient method for leukemia detection, though it is incremental as it builds on existing hypercomplex-valued neural network research.
The paper tackled the problem of diagnosing acute lymphoblastic leukemia by comparing real-valued and quaternion-valued convolutional neural networks on microscopic blood smear images, finding that the quaternion-valued network achieved similar or better performance using only 34% of the parameters.
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.