A New Pairwise Deep Learning Feature For Environmental Microorganism Image Analysis

TL;DR

This paper introduces a novel pairwise deep learning feature extraction method for environmental microorganism image analysis, combining handcrafted interest points with deep features to improve classification accuracy and consistency.

Contribution

The paper proposes a new pairwise deep learning feature technique that integrates handcrafted interest points with deep features using geometric pairing, enhancing microorganism classification performance.

Findings

Achieved 99.17% accuracy in microorganism classification.

Improved F1-score by 62.40% over non-paired features.

Enhanced precision and recall with significant gains.

Abstract

Environmental microorganism (EM) offers a high-efficient, harmless, and low-cost solution to environmental pollution. They are used in sanitation, monitoring, and decomposition of environmental pollutants. However, this depends on the proper identification of suitable microorganisms. In order to fasten, low the cost, increase consistency and accuracy of identification, we propose the novel pairwise deep learning features to analyze microorganisms. The pairwise deep learning features technique combines the capability of handcrafted and deep learning features. In this technique we, leverage the Shi and Tomasi interest points by extracting deep learning features from patches which are centered at interest points locations. Then, to increase the number of potential features that have intermediate spatial characteristics between nearby interest points, we use Delaunay triangulation theorem and straight-line geometric theorem to pair the nearby deep learning features. The potential of pairwise features is justified on the classification of EMs using SVMs, k-NN, and Random Forest classifier. The pairwise features obtain outstanding results of 99.17%, 91.34%, 91.32%, 91.48%, and 99.56%, which are the increase of about 5.95%, 62.40%, 62.37%, 61.84%, and 3.23% in accuracy, F1-score, recall, precision, and specificity respectively, compared to non-paired deep learning features.