Malaria Blood Cell Image Classification using Transfer Learning with Fine-Tune ResNet50 and Data Augmentation

  • Aris Muhandisin Universitas Muhammadiyah Malang
  • Yufis Azhar Universitas Muhammadiyah Malang
Keywords: Malaria, Image Classification, Convolutional Neural Network, Fine-Tuning, ResNet50


Based on the WHO Report related to malaria, it is estimated that there will be 241 million malaria cases and 627,000 deaths from this disease globally in 2020 with the number of deaths increasing yearly. Preventing malaria disease conditions is through early detection. A more quick and precise malaria diagnosis method was required to simplify and reduce the detection process. Medical image classification could be carried out rapidly and precisely using machine learning or deep learning techniques. This research aims to diagnose malaria by classifying images of malaria blood cells using Deep Learning with a Transfer Learning approach. By utilizing various fine-tuning procedures and implementing data augmentation proposed method develops the method from previous studies. Two types of models Frozen ResNet50 and Fine-Tune ResNet50 are being tested. The dataset utilized will be augmented to improve model performance. This study makes use of the "NIH Malaria Cell Images Dataset" a dataset that contains a total of 27,660 image data. It is divided into two classes: parasitized and uninfected. The results are improved from previous research using the fine-tuned VGG16 model with an accuracy of 96% compared to this study using the fine-tuned ResNet50 model which achieved an accuracy score of 98%.


Download data is not yet available.


P. M. Sharp, L. J. Plenderleith, and B. H. Hahn, “Ape Origins of Human Malaria,” Annu. Rev. Microbiol., vol. 74, p. 39, Sep. 2020, doi: 10.1146/ANNUREV-MICRO-020518-115628.

“World malaria report 2021.” (accessed Jul. 11, 2022).

“Sebanyak 94.610 Kasus Malaria Terjadi di Indonesia pada 2021 | Databoks.” (accessed Jul. 11, 2022).

“CDC - Parasites - Malaria.” (accessed Jul. 11, 2022).

K. Roy, S. Sharmin, R. B. Mufiz Mukta, and A. Sen, “Detection of Malaria Parasite in Giemsa Blood Sample Using Image Processing,” Int. J. Comput. Sci. Inf. Technol., vol. 10, no. 1, pp. 55–65, Oct. 2018, doi: 10.5121/IJCSIT.2018.10105.

L. Cai, J. Gao, and D. Zhao, “A review of the application of deep learning in medical image classification and segmentation,” Ann. Transl. Med., vol. 8, no. 11, pp. 713–713, Jun. 2020, doi: 10.21037/ATM.2020.02.44.

P. K. Maduri, Shalu, S. Agrawal, A. Rai, and S. Chaubey, “Malaria Detection Using Image Processing and Machine Learning,” Proc. - 2021 3rd Int. Conf. Adv. Comput. Commun. Control Networking, ICAC3N 2021, pp. 1789–1792, Jan. 2018, doi: 10.48550/arxiv.1801.10031.

N. Abbas et al., “Plasmodium life cycle stage classification based quantification of malaria parasitaemia in thin blood smears,” Microsc. Res. Tech., vol. 82, no. 3, pp. 283–295, Mar. 2019, doi: 10.1002/JEMT.23170.

M. Poostchi, K. Silamut, R. J. Maude, S. Jaeger, and G. Thoma, “Image analysis and machine learning for detecting malaria,” Transl. Res., vol. 194, p. 36, Apr. 2018, doi: 10.1016/J.TRSL.2017.12.004.

Muhathir, R. A. Rizal, J. S. Sihotang, and R. Gultom, “Comparison of SURF and HOG extraction in classifying the blood image of malaria parasites using SVM,” 2019 Int. Conf. Comput. Sci. Inf. Technol. ICoSNIKOM 2019, Nov. 2019, doi: 10.1109/ICOSNIKOM48755.2019.9111647.

R. Malhotra, D. Joshi, and K. Y. Shin, “Approaching Bio Cellular Classification for Malaria Infected Cells Using Machine Learning and then Deep Learning to compare & analyze K-Nearest Neighbours and Deep CNNs,” May 2020, Accessed: Jul. 11, 2022. [Online]. Available:

S. C. Kalkan and O. K. Sahingoz, “Deep learning based classification of malaria from slide images,” 2019 Sci. Meet. Electr. Biomed. Eng. Comput. Sci. EBBT 2019, Apr. 2019, doi: 10.1109/EBBT.2019.8741702.

Vijayalakshmi A and Rajesh Kanna B, “Deep learning approach to detect malaria from microscopic images,” Multimed. Tools Appl. 2019 7921, vol. 79, no. 21, pp. 15297–15317, Jan. 2019, doi: 10.1007/S11042-019-7162-Y.

S. Rajaraman et al., “Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images,” PeerJ, vol. 6, no. 4, 2018, doi: 10.7717/PEERJ.4568.

G. Shekar, S. Revathy, and E. K. Goud, “Malaria Detection using Deep Learning,” Proc. 4th Int. Conf. Trends Electron. Informatics, ICOEI 2020, pp. 746–750, Jun. 2020, doi: 10.1109/ICOEI48184.2020.9143023.

A. Maqsood, M. S. Farid, M. H. Khan, and M. Grzegorzek, “Deep Malaria Parasite Detection in Thin Blood Smear Microscopic Images,” Appl. Sci. 2021, Vol. 11, Page 2284, vol. 11, no. 5, p. 2284, Mar. 2021, doi: 10.3390/APP11052284.

T. Fatima and M. S. Farid, “Automatic detection of Plasmodium parasites from microscopic blood images,” J. Parasit. Dis. 2019 441, vol. 44, no. 1, pp. 69–78, Sep. 2019, doi: 10.1007/S12639-019-01163-X.

Y. M. Kassim, F. Yang, H. Yu, R. J. Maude, and S. Jaeger, “Diagnosing Malaria Patients with Plasmodium falciparum and vivax Using Deep Learning for Thick Smear Images,” Diagnostics 2021, Vol. 11, Page 1994, vol. 11, no. 11, p. 1994, Oct. 2021, doi: 10.3390/DIAGNOSTICS11111994.

E. T. Efaz, F. Alam, and M. S. Kamal, “Deep cnn-supported ensemble cadx architecture to diagnose malaria by medical image,” Adv. Intell. Syst. Comput., vol. 1309, pp. 231–243, 2021, doi: 10.1007/978-981-33-4673-4_20/COVER/.

A. E. Minarno, Y. Azhar, F. D. Setiawan Sumadi, and Y. Munarko, “A Robust Batik Image Classification using Multi Texton Co-Occurrence Descriptor and Support Vector Machine,” 2020 3rd Int. Conf. Intell. Auton. Syst. ICoIAS 2020, pp. 51–55, Feb. 2020, doi: 10.1109/ICOIAS49312.2020.9081833.

A. Sai Bharadwaj Reddy and D. Sujitha Juliet, “Transfer learning with RESNET-50 for malaria cell-image classification,” Proc. 2019 IEEE Int. Conf. Commun. Signal Process. ICCSP 2019, pp. 945–949, Apr. 2019, doi: 10.1109/ICCSP.2019.8697909.

“Malaria Cell Images Dataset | Kaggle.” (accessed Jul. 12, 2022).

“LHNCBC Full Download List.” (accessed Jul. 12, 2022).

P. Chlap, H. Min, N. Vandenberg, J. Dowling, L. Holloway, and A. Haworth, “A review of medical image data augmentation techniques for deep learning applications,” J. Med. Imaging Radiat. Oncol., vol. 65, no. 5, pp. 545–563, Aug. 2021, doi: 10.1111/1754-9485.13261.

A. Mikołajczyk and M. Grochowski, “Data augmentation for improving deep learning in image classification problem,” 2018 Int. Interdiscip. PhD Work. IIPhDW 2018, pp. 117–122, Jun. 2018, doi: 10.1109/IIPHDW.2018.8388338.

T. Jameela, K. Athotha, N. Singh, V. K. Gunjan, and S. Kahali, “Deep Learning and Transfer Learning for Malaria Detection,” Comput. Intell. Neurosci., vol. 2022, pp. 1–14, Jun. 2022, doi: 10.1155/2022/2221728.

S. A. Elghany, M. Ramadan, M. Elmogy, M. R. Ibraheem, and M. Alruwaili, “Diagnosis of Various Skin Cancer Lesions Based on Fine-Tuned ResNet50 Deep Network”, doi: 10.32604/cmc.2021.016102.

D. Misra, “Mish: A Self Regularized Non-Monotonic Activation Function,” Aug. 2019, doi: 10.48550/arxiv.1908.08681.

E. M. Dogo, O. J. Afolabi, N. I. Nwulu, B. Twala, and C. O. Aigbavboa, “A Comparative Analysis of Gradient Descent-Based Optimization Algorithms on Convolutional Neural Networks,” Proc. Int. Conf. Comput. Tech. Electron. Mech. Syst. CTEMS 2018, pp. 92–99, Dec. 2018, doi: 10.1109/CTEMS.2018.8769211.

S. Himori et al., “Comparative study of optimization techniques in deep learning: Application in the ophthalmology field,” J. Phys. Conf. Ser., vol. 1743, no. 1, p. 012002, Jan. 2021, doi: 10.1088/1742-6596/1743/1/012002.

Z. Shi, X. Xu, X. Liu, J. Chen, and M.-H. Yang, “Video Frame Interpolation Transformer,” Nov. 2021, doi: 10.48550/arxiv.2111.13817.

How to Cite
Aris Muhandisin, & Azhar, Y. (2022). Malaria Blood Cell Image Classification using Transfer Learning with Fine-Tune ResNet50 and Data Augmentation. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 6(5), 891 - 897.
Information Technology Articles