人工智能
计算机科学
硅藻
模式识别(心理学)
卷积神经网络
截锥
计算机视觉
图像(数学)
作者
Carlos Sánchez,Noelia Vallez,Gloria Bueno,Gabriel Cristóbal
标识
DOI:10.1007/978-3-030-31332-6_28
摘要
Diatoms are a major group of aquatic microalgae. They are widely used in different fields such as environmental studies to estimate water quality. This paper presents the use of convolutional neural networks (CNNs) to identify diatoms during their life cycle. This life cycle involves morphological and other changes to the diatom frustule adding intraclass variance and making harder the classification task. The performance of CNNs is compared against a classical image classification scheme (i.e., feature extraction and classification) using a 14 classes dataset with a total number of 1085 images ranging from 40 to 120 images per class. Classification accuracy was 99.07% and 99.7% for CNNs and classical methods respectively.
科研通智能强力驱动
Strongly Powered by AbleSci AI