论文标题
使用超复合价值的卷积神经网络检测急性淋巴细胞白血病检测
Acute Lymphoblastic Leukemia Detection Using Hypercomplex-Valued Convolutional Neural Networks
论文作者
论文摘要
本文的特征是在用于对血液涂片数字显微镜图像中淋巴细胞进行分类的超复合代数定义的卷积神经网络。这种分类有助于诊断急性淋巴细胞白血病（ALL），一种血液癌。我们使用八个超复杂值的卷积神经网络（HVCNN）以及实值卷积网络执行分类任务。我们的结果表明，HVCNN的性能优于实值模型，以较小的参数显示更高的精度。此外，我们发现基于Clifford代数处理HSV编码的图像获得了最高观察到的精度的HVCNN。确切地说，我们的HVCNN使用All-IDB2数据集的平均准确性率为96.6％，其列车测试拆分为50％，这一值非常接近最先进的模型，但使用更简单的体系结构，参数较少。
This paper features convolutional neural networks defined on hypercomplex algebras applied to classify lymphocytes in blood smear digital microscopic images. Such classification is helpful for the diagnosis of acute lymphoblast leukemia (ALL), a type of blood cancer. We perform the classification task using eight hypercomplex-valued convolutional neural networks (HvCNNs) along with real-valued convolutional networks. Our results show that HvCNNs perform better than the real-valued model, showcasing higher accuracy with a much smaller number of parameters. Moreover, we found that HvCNNs based on Clifford algebras processing HSV-encoded images attained the highest observed accuracies. Precisely, our HvCNN yielded an average accuracy rate of 96.6% using the ALL-IDB2 dataset with a 50% train-test split, a value extremely close to the state-of-the-art models but using a much simpler architecture with significantly fewer parameters.
