Bacteria Classification using Image Processing and Residual Neural Network (ResNet)

Dybio Dompu Hot Asih, Adnan Purwanto, Dwiza Riana, Sri Hadianti


Detection of microorganisms is of particular importance to human health and life, and for the industry in general. For this reason, we want this process to be as fast and precise as possible. We also expect that the automation of this activity (detection of microorganisms) can be widely used in various industries. This article is another attempt to the classification of bacteria  that uses a deep learning approach with Residual Neural Network(ResNet) models. The research was conducted by training the ResNet-18,ResNet-34, ResNet50 and ResNet-101 models. The results show that the ResNet-50 and ResNet-101 are the best learning model. It is better to use ResNet-50 than ResNet-101 because of the faster training time. While the results of the research also show that the architecture with the least number of layers is the fastest learning model.  ResNet-50 has an accuracy rate of 96.1% with a training time of 451 seconds is the best learning model. ResNet-18 has an accuracy rate of 93.6% with a training time of 185 seconds is the fastest learning model.

Full Text:



A. Wibisono, J. Rachmad, and E. Anderson, “Deep Learning and Classic Machine Learning Approach for Automatic Bone Age Assessment,” 2019 4th Asia-Pacific Conf. Intell. Robot Syst., pp. 235–240, 2019.

C. Rujichan, “Bacteria Classification using Image Processing and Deep Convolutional Neural Network,” 2019.

I. W. S. E. P, A. Y. Wijaya, and R. Soelaiman, “Klasifikasi Citra Menggunakan Convolutional Neural Network ( Cnn ) pada Caltech 101,” vol. 5, no. 1, 2016.

A. Khan, A. Sohail, U. Zahoora, and A. S. Qureshi, “A Survey of the Recent Architectures of Deep Convolutional Neural Networks 1 Introduction,” pp. 1–70.

D. Joshi, “Approaching Bio Cellular Classification for Malaria Infected Cells Using Machine Learning and then Deep Learning to compare & analyze K-Nearest Neighbours and Deep CNNs,” 2017.

S. B. Griesemer and G. Van Slyke, “Assessment of Sample Pooling for Clinical SARS-CoV-2 Testing,” no. January, 2021.

A. Kour, “A Review on Image Processing,” vol. 4, no. 1, pp. 270–275, 2013.

B. Zieli, A. Plichta, K. Misztal, P. Spurek, M. Brzychczy-włoch, and D. Ocho, “Deep learning approach to bacterial colony classification,” 2017.

B. A. Mohamed and H. M. Afify, “Automated classification of Bacterial Images extracted from Digital Microscope via Bag of Words Model,” no. 1, pp. 1–4.

I. Transactions and O. N. Systems, “Efficient and Rapid Machine Learning Algorithms for Big Data and Dynamic Varying Systems,” pp. 1–2, 2017.

A. Buslaev, V. I. Iglovikov, E. Khvedchenya, A. Parinov, M. Druzhinin, and A. A. Kalinin, “Albumentations: Fast and flexible image augmentations,” Inf., vol. 11, no. 2, 2020, doi: 10.3390/info11020125.

S. R. G. B. Grayscale, “Perbaikan Hasil Segmentasi Hsv Pada Citra Digital Menggunakan Metode Segmentasi Rgb Grayscale,” Edu Komputika J., vol. 6, no. 1, pp. 32–37, 2019, doi: 10.15294/edukomputika.v6i1.23025.

F. Ramzan et al., “A Deep Learning Approach for Automated Diagnosis and Multi-Class Classification of Alzheimer’s Disease Stages Using Resting-State fMRI and Residual Neural Networks,” J. Med. Syst., vol. 44, no. 2, 2020, doi: 10.1007/s10916-019-1475-2.

S. B. Kotsiantis, I. D. Zaharakis, and P. E. Pintelas, “Machine learning: A review of classification and combining techniques,” Artif. Intell. Rev., vol. 26, no. 3, pp. 159–190, 2006, doi: 10.1007/s10462-007-9052-3.

J. Feys, “Nonparametric Tests for the Interaction in Two-way Factorial Designs Using R,” vol. 8, no. 2008, pp. 367–378, 2016.

A. Calle-Saldarriaga, H. Laniado, and F. Zuluaga, “Homogeneity Test for Functional Data based on Data-Depth Plots,” pp. 1–25, 2020.

Ö. Karadaǧ and S. Aktaş, “Optimal sample size determination for the ANOVA designs,” Int. J. Appl. Math. Stat., vol. 25, no. 1, pp. 127–134, 2012.



  • There are currently no refbacks.

Copyright (c) 2022 Dybio Dompu Hot Asih, Adnan Purwanto, Dwiza Riana, Sri Hadianti

Editorial Address:

Kampus Raja Ali Haji
Gedung Fakultas Sains & Teknologi UIN Suska Riau
Jl.H.R.Soebrantas No.155 KM 18 Simpang Baru Panam, Pekanbaru 28293
© 2023 SITEKIN, ISSN 2407-0939

SITEKIN Journal Indexing:

Google Scholar | Garuda | Moraref | IndexCopernicus | SINTA

Creative Commons License