Hoorali Fatemeh, Khosravi Hossein, Moradi Bagher
Faculty of Electrical Engineering and Robotics, Shahrood University of Technology, Shahrood, Iran.
Faculty of Electrical Engineering and Robotics, Shahrood University of Technology, Shahrood, Iran.
J Microbiol Methods. 2020 Oct;177:106056. doi: 10.1016/j.mimet.2020.106056. Epub 2020 Sep 12.
Anthrax is one of the important diseases in humans and animals, caused by the gram-positive bacteria spores called Bacillus anthracis. The disease is still one of the health problems of developing countries. Due to fatigue and decreased visual acuity, microscopic diagnosis of diseases by humans may not be of good quality. In this paper, for the first time, a system for automatic and rapid diagnosis of anthrax disease simultaneously with detection and segmentation of B. anthracis bacteria in microscopic images has been proposed based on artificial intelligence and deep learning techniques. Two important architectures of deep neural networks including UNet and UNet++ have been used for detection and segmentation of the most important component of the image i.e. bacteria. Automated detection and segmentation of B. anthracis bacteria offers the same level of accuracy as the human diagnostic specialist and in some cases outperforms it. Experimental results show that these deep architectures especially UNet++ can be used effectively and efficiently to automate B. anthracis bacteria segmentation of microscopic images obtained under different conditions. UNet++ produces outstanding results despite the many challenges in this field, such as high image dimension, image artifacts, object crowding, and overlapping. We conducted our experiments on a dataset prepared privately and achieved an accuracy of 97% and the dice score of 0.96 on the patch test images. It also tested on whole raw images and a recall of 98% and accuracy of 97% is achieved, which shows excellent performance in the bacteria segmentation task. The low cost and high speed of diagnosis and no need for a specialist are other benefits of the proposed system.
炭疽病是人和动物的重要疾病之一，由革兰氏阳性细菌芽孢杆菌引起。该疾病仍然是发展中国家的健康问题之一。由于疲劳和视力下降，人工进行疾病的显微镜诊断质量可能不高。本文首次提出了一种基于人工智能和深度学习技术的系统，用于在显微镜图像中自动快速诊断炭疽病，同时检测和分割炭疽芽孢杆菌。深度神经网络的两种重要架构，即U-Net和U-Net++，已被用于检测和分割图像中最重要的成分，即细菌。炭疽芽孢杆菌的自动检测和分割提供了与人类诊断专家相同的准确度，并且在某些情况下优于专家。实验结果表明，这些深度架构，尤其是U-Net++，可以有效且高效地用于自动化在不同条件下获得的显微镜图像中炭疽芽孢杆菌的分割。尽管该领域存在许多挑战，如高图像维度、图像伪影、物体拥挤和重叠，U-Net++仍产生了出色的结果。我们在一个私人准备的数据集上进行了实验，在补丁测试图像上达到了97%的准确度和0.96的骰子系数。它还在整个原始图像上进行了测试，召回率达到98%，准确度达到97%，这在细菌分割任务中显示出了出色的性能。该系统成本低、诊断速度快且无需专家也是其其他优点。
