Bacterial Classification Using Deep Structured Convolutional Neural Network for Low Resource Data

  M Faizal Amri (1*), Asri Rizki Yuliani (2), Dwi Esti Kusumandari (3), Artha Ivonita Simbolon (4), M. Ilham Rizqyawan (5), Ulfah Nadiya (6)

(1) Research Center for Smart Mechatronic, National Research and Innovation Agency - Indonesia orcid
(2) Research Center for Artificial Intelligence and Cyber Security, National Research and Innovation Agency - Indonesia
(3) Research Center for Smart Mechatronic, National Research and Innovation Agency - Indonesia
(4) Research Center for Smart Mechatronic, National Research and Innovation Agency - Indonesia
(5) Research Center for Smart Mechatronic, National Research and Innovation Agency - Indonesia
(6) Research Center for Smart Mechatronic, National Research and Innovation Agency - Indonesia
(*) Corresponding Author

Received: January 19, 2023; Revised: March 31, 2023
Accepted: April 26, 2023; Published: August 31, 2023

How to cite (IEEE): M. Amri, A. R. Yuliani, D. E. Kusumandari, A. I. Simbolon, M. Rizqyawan,  and U. Nadiya, "Bacterial Classification Using Deep Structured Convolutional Neural Network for Low Resource Data," Jurnal Elektronika dan Telekomunikasi, vol. 23, no. 1, pp. 47-54, Aug. 2023. doi: 10.55981/jet.533


Bacterial identification is an essential task in medical disciplines and food hygiene. The characteristics of bacteria can be examined under a microscope using culture techniques. However, traditional clinical laboratory culture methods require considerable work, primarily physical and manual effort. An automated process using deep learning technology has been widely used for increasing accuracy and decreasing working costs. In this paper, our research evaluates different types of existing deep CNN models for bacterial contamination classification when low-resource data are used. They are baseline CNN, GCNN, ResNet, and VGGNet. The performance of CNN models was also compared with the traditional machine learning method, including SIFT+SVM. The performance of the DIBaS dataset and our own collected dataset have been evaluated. The results show that VGGNet achieves the highest accuracy. In addition, data augmentation was performed to inflate the dataset. After fitting the model with augmented data, the results show that the accuracy increases significantly. This improvement is consistent in all models and both datasets.



Bacterial classification; Deep learning; Convolutional neural network (CNN); E-coli

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