Context-adaptive local binary pattern for bacterial microscopic image classification using lightweight neural networks

Authors

https://doi.org/10.22105/thi.v2i3.59

Abstract

Accurate classification of bacterial species from microscopic images is important for computer-assisted microbial diagnosis, particularly when conventional identification methods are time-consuming and require specialized expertise. This study proposes a Context-Adaptive Local Binary Pattern (CA-LBP) approach for extracting discriminative texture features from bacterial microscopic images. Unlike conventional Local Binary Patterns (LBP), the proposed method adaptively selects the LBP representation according to the local texture characteristics of the image. The extracted features are evaluated using two classification frameworks: a Multilayer Perceptron (MLP) based on the CA-LBP feature vector and a lightweight Convolutional Neural Network (CNN) based on the corresponding CA-LBP feature map. Experiments were conducted on the DIBaS dataset after excluding Candida albicans, resulting in 32 bacterial classes. The CA-LBP+CNN framework achieved the best performance among the evaluated methods, with an accuracy of 92.25%, sensitivity of 94.86%, precision of 91.63%, and F-measure of 93.57%. The results indicate that adaptive texture representation can effectively complement convolutional feature learning for bacterial microscopic image classification, particularly in a limited-data setting. The proposed framework provides a lightweight alternative to conventional deep-learning-based approaches while retaining the discriminative characteristics of local texture patterns.

Keywords:

Bacterial microscopic image classification, Context-adaptive LBP, Texture analysis, Convolutional neural network

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Published

2025-09-16

Issue

Vol. 2 No. 3 (2025): Trends in Health Informatics

Section

Articles

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How to Cite

Shakila Devi, G. T. . (2025). Context-adaptive local binary pattern for bacterial microscopic image classification using lightweight neural networks. Trends in Health Informatics, 2(3), 158-170. https://doi.org/10.22105/thi.v2i3.59

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