Signals and Communications Department (DSC), Institute for Technological Development and Innovation in Communications (IDeTIC), University of Las Palmas de Gran Canaria (ULPGC), Las Palmas de Gran Canaria, 35001 Canary Islands, Spain.
Department of Telecommunications, Faculty of Electrical Engineering and Communication, Brno University of Technology (BUT), 61600 Brno, Czech Republic.
Sensors (Basel). 2021 Apr 28;21(9):3068. doi: 10.3390/s21093068.
The use of image processing tools, machine learning, and deep learning approaches has become very useful and robust in recent years. This paper introduces the detection of the Nosema disease, which is considered to be one of the most economically significant diseases today. This work shows a solution for recognizing and identifying Nosema cells between the other existing objects in the microscopic image. Two main strategies are examined. The first strategy uses image processing tools to extract the most valuable information and features from the dataset of microscopic images. Then, machine learning methods are applied, such as a neural network (ANN) and support vector machine (SVM) for detecting and classifying the Nosema disease cells. The second strategy explores deep learning and transfers learning. Several approaches were examined, including a convolutional neural network (CNN) classifier and several methods of transfer learning (AlexNet, VGG-16 and VGG-19), which were fine-tuned and applied to the object sub-images in order to identify the Nosema images from the other object images. The best accuracy was reached by the VGG-16 pre-trained neural network with 96.25%.
近年来，图像处理工具、机器学习和深度学习方法的应用变得非常有用和强大。本文介绍了对被认为是当今经济意义最重大的疾病之一的微孢子虫病的检测。这项工作展示了一种在显微镜图像中的其他现有物体之间识别和鉴定微孢子虫细胞的解决方案。研究了两种主要策略。第一种策略使用图像处理工具从显微镜图像数据集提取最有价值的信息和特征。然后，应用机器学习方法，如神经网络 (ANN) 和支持向量机 (SVM)，用于检测和分类微孢子虫病细胞。第二种策略探索了深度学习和迁移学习。检查了几种方法，包括卷积神经网络 (CNN) 分类器和几种迁移学习方法（AlexNet、VGG-16 和 VGG-19），对目标子图像进行了微调，并将其应用于这些图像，以将微孢子虫图像与其他物体图像区分开来。VGG-16 预先训练的神经网络达到了 96.25%的最佳准确率。
