Journal Article

Machine Learning and Deep Learning Based Hybrid Feature Extraction and Classification Model Using Digital Microscopic Bacterial Images

Kotwal, Shallu, Rani, Priya, Arif, Tasleem, Manhas, Jatinder

2023

Overview

Bacteria are single-celled organisms with a propensity to survive in a wide range of environments. Most of these species can be found both in soil and in oceans whereas some of them are also present in the human body. Majority of bacterial species are harmful to humans, producing a wide range of infectious diseases like cholera, strep throat, tuberculosis, etc. Only a small minority of bacterial species are beneficial to humans. Thus, the study of bacteria is extremely important to analyze, identify benefits and to get rid of their negative effects. Microbiologist uses slide culturing process for bacteria identification, which involve microscope for examination of various species of bacteria. As a result, the shapes of the various samples differ, and to distinguish one sample from another, several characteristics, such as differences in cellular structure and cell-component divergence, are seen. This process is time-consuming and labor-intensive and significantly dependent on expensive machinery and human expertise. A variety of defects and problems can be easily remedied with the development and widespread use of machine learning-based computer assisted solutions in this area. The model developed with the help of machine learning tools and technologies is particularly effective in this field of image analysis and has shown an extraordinarily high rate of improvement in clinical microbiology research by recognizing different bacterial species. For more precise and better outcomes in the categorization of bacteria, feature extraction from digital images is crucial and incredibly vital. The method of feature extraction helps to eliminate extraneous data from a data collection, which speeds up learning and generalization throughout the entire machine learning process. In this paper, we try to attempt various machine learning methods to build an ensembled feature extractor and selector for better classification of microscopic bacterial Images. In this paper, we attempt to compare different feature extraction algorithms with various machine learning classifiers. The experiments have been performed on a novel dataset comprising microscopic images of four bacteria species i.e. Acetobacter aceti , Micrococcus luteus , Bacillus anthracis and Thermus sp. For feature extraction HOG (Histogram of Oriented Gradients), LBP (Local Binary Pattern), ResNet50 and VGG16 techniques have been employed. Using these features performance of five classification algorithms i.e., SVM (Support Vector Machine), RF (Random Forest), Naïve Bayes, Decision Tree and KNN (K-Nearest Neighbor) has been compared. Moreover, comparison has also been performed among feature extraction techniques also. The experimental results show that the combination of SVM and the deep features extracted using VGG16 outperformed other techniques in terms of different classification performance measures, and achieved an accuracy of 99.89%.

• Publisher: Springer Nature Singapore,Springer Nature B.V
• Subject: Accuracy / Algorithms / Artificial intelligence / Bacteria / Bacterial diseases / Cellular structure / Cholera / Classification / Computer Imaging / Computer Science / Computer Systems Organization and Communication Networks / Data collection / Data Structures and Information Theory / Datasets / Decision trees / Deep learning / Digital imaging / Divergence / Feature extraction / Image analysis / Infectious diseases / Information Systems and Communication Service / Literature reviews / Machine learning / Machine Learning Modeling Techniques and Applications / Microbiology / Microorganisms / Oceans / Original Research / Pattern Recognition and Graphics / Research methodology / Software Engineering/Programming and Operating Systems / Support vector machines / Tropical diseases / Tuberculosis / Vision