Automatic Vaginal Bacteria Segmentation and Classification Based on Superpixel and Deep Learning

In this paper, a new method for automatic vaginal bacteria cell segmentation and classification is proposed. Segmentation algorithm based on superpixel is first investigated to segment region of interest of the input image into cells. Feature extraction based on the segmented regions is trained by supervised deep learning method. Four types of different bacteria are studied for classification. Our experimental results show the classification result yields an accuracy of 99%, sensitivity of 100% and specificity of 98.04%. Compared to the state-of-the-arts method, better segmentation results have been achieved. Furthermore, our comparative analysis also shows that deep learning method outperforms traditional methods such as neural network and support vector machine.