Automatic Bacillus anthracis bacteria detection and segmentation in microscopic images using UNet+

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.