An image analysis-aided method for redundancy reduction in differentiation of identical Actinobacterial strains

Abstract

Aim: To simplify the recognition of Actinobacteria, at different stages of the growth phase, from a mixed culture to facilitate the isolation of novel strains of these bacteria for drug discovery purposes. Materials & methods: A method was developed based on Gabor transform, and machine learning using k-Nearest Neighbors and Naive Bayes classifier, Logitboost, Bagging and Random Forest to automatically categorize the colonies. Results: A signature pattern was inferred by the model, making the differentiation of identical strains possible. Additionally, higher performance, compared with other classification methods was achieved. Conclusion: This automated approach can contribute to the acceleration of the drug discovery process while it simultaneously can diminish the loss of budget due to the redundancy occurred by the inexperienced researchers.