Microorganism image classification with circle-based Multi-Region Binarization and mutual-information-based feature selection

Jiawen, Luo; Ser, Wee; Liu, A.; Liedberg, Bo; Rayatpisheh, Shahrzad

doi:10.1016/j.bea.2021.100020

Cited by 5 publications

(1 citation statement)

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“…This study shows that color features have discriminatory properties and have a significant effect on improving image recognition. On the other hand, Luo et al [8] used segmented images to extract the shape and size of microorganisms and then classify them based on their type. Ernest Bonah [9] introduced an approach to identifying food-related pathogens based on the dispersal patterns of bacterial colonies.…”

Section: Introductionmentioning

confidence: 99%

Innovative Bacterial Colony Detection: Leveraging Multi-Feature Selection with the Improved Salp Swarm Algorithm

Ihsan,

Muttaqin,

Fajri

et al. 2023

J. Imaging

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In this paper, we introduce a new and advanced multi-feature selection method for bacterial classification that uses the salp swarm algorithm (SSA). We improve the SSA’s performance by using opposition-based learning (OBL) and a local search algorithm (LSA). The proposed method has three main stages, which automate the categorization of bacteria based on their unique characteristics. The method uses a multi-feature selection approach augmented by an enhanced version of the SSA. The enhancements include using OBL to increase population diversity during the search process and LSA to address local optimization problems. The improved salp swarm algorithm (ISSA) is designed to optimize multi-feature selection by increasing the number of selected features and improving classification accuracy. We compare the ISSA’s performance to that of several other algorithms on ten different test datasets. The results show that the ISSA outperforms the other algorithms in terms of classification accuracy on three datasets with 19 features, achieving an accuracy of 73.75%. Additionally, the ISSA excels at determining the optimal number of features and producing a better fit value, with a classification error rate of 0.249. Therefore, the ISSA method is expected to make a significant contribution to solving feature selection problems in bacterial analysis.

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Section: Introductionmentioning

confidence: 99%