Classifying Bacterial Species using Computer Vision and Machine Learning

Abstract

The study embodied in this paper, aims at making use of machine learning and computer vision algorithms in order to reliably identify the species of bacteria, from their microscopic images. The study has taken into consideration three of the most commonly occurring species of bacteria that are clinically important. The work shown further in this study can be extended to a larger number of bacteria species. The study makes use of the Speeded Up Robust Features or SURF algorithm for detecting image keypoints. The artificial intelligence classifier makes use of these keypoint vectors as its input variable. It is noteworthy that the images used are taken at the gram staining stage of bacterial identification, in order to minimize any biases in the dataset owing to a variance in staining technique, although the feature detector invariably grayscales the image prior to keypoint computation. The paper follows the study from the conception of the idea, to the formulating of the algorithm, the training and testing of the same.

Quantum Mechanism-Based Convolution Model for the Classification of Pathogenic Bacteria

“…It used only 400 images of the DIBaS dataset. [31] used naïve Bayes algorithm and canny edge detection to classify bacteria in low training time.…”