Automated Life Stage Classification of Malaria Using Deep Learning


  • Janjhyam Venkata Naga Ramesh Koneru Lakshmaiah Education Foundation image/svg+xml
  • Raghav Agarwal Vellore Institute of Technology University image/svg+xml
  • Harshitha Jyasta Koneru Lakshmaiah Education Foundation image/svg+xml
  • Bommisetty Sivani Koneru Lakshmaiah Education Foundation image/svg+xml
  • Palacholla Anuradha Sri Tulasi Mounika Koneru Lakshmaiah Education Foundation image/svg+xml
  • Bollineni Bhargavi Koneru Lakshmaiah Education Foundation image/svg+xml



Deep learning, Malaria microscopic cell images, life stage classification, Blood smear


INTRODUCTION: Malaria, an infectious illness spread by mosquitoes, is a serious hazard to humans and animals, with an increasing number of cases recorded yearly. Prompt and precise diagnosis, as well as preventative actions, are critical for effectively combating this condition. Malaria is now diagnosed using standard techniques. Microscopy of blood smears, which consists of small pictures, is used by trained specialists to identify diseased cells and define their life phases. The World Health Organisation (WHO) has approved this microscopy-based malaria diagnostic method. Drawing a blood sample from the finger, pricking it, spreading it onto a clean glass slide, and allowing it to dry naturally are all steps in the method. Thin blood smears were previously used to identify parasites under the microscope, but thick blood smears are utilized when parasite levels are low.

OBJECTIVES: Due to its reliance on medical knowledge, high prices, time-consuming nature, and unsatisfactory outcomes, this technique has significant disadvantages. However, as deep learning algorithms progress, these activities may be completed more effectively and with fewer human resources.

METHODS: This study demonstrates the usefulness of transfer learning, a type of deep learning, in categorizing microscopic pictures of parasitized versus uninfected malaria cells. Six models were evaluated using the publicly accessible NIH dataset, proving the usefulness of the suggested technique.

RESULTS: VGG19 model fared better than its competitors, obtaining 95.05% accuracy, 92.83% precision, 96.88% sensitivity, 93.46% specificity, and 94.81% F1-score.

CONCLUSION: This categorization of malaria cell photos will benefit microscopists in particular, as it will improve their workflow and provide a viable alternative for detecting malaria using microscopic cell images.


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WHO, World Malaria Report-2020 (Accessed on 2021, June18) Available: programme/reports/world-malaria-report-2020.

"Peripheral_blood_smear_-_stained_and_unstained.jpg (2460×3372)." _smear_-_stained_and_unstained.jpg (accessed May 22, 2021).

"Malaria.", World Health Organization, 01 April 2021, May 02, 2021).

CDC. (2019) About Malaria. [Online].

"Malaria." (accessed Apr. 05, 2021).

M. L. Wilson, "Malaria rapid diagnostic tests," Clin. Infect. Dis., vol. 54, no. 11, pp. 1637–1641, 2012, doi: 10.1093/cid/cis228. DOI:

S. D. Pande and R. Agarwal, "Multi-Class Kidney Abnormalities Detecting Novel System Through Computed Tomography," in IEEE Access, doi: 10.1109/ACCESS.2024.3351181. DOI:

R. Agarwal, S. D. Pande, S. N. Mohanty and S. K. Panda, "A Novel Hybrid System of Detecting Brain Tumors in MRI," in IEEE Access, vol. 11, pp. 118372-118385, 2023, doi: 10.1109/ACCESS.2023.3326447. DOI:

R. Agarwal and D. Godavarthi, “Skin Disease Classification Using CNN Algorithms”, EAI Endorsed Trans Perv Health Tech, vol. 9, Oct. 2023. DOI:

ASB Reddy and DS Juliet, “Transfer learning with ResNet-50 for malaria cell-image classification,” International Conference on Communication and Signal Processing (ICCSP), pp. 945-949, 2019.

SC Kalkan and OK Sahingoz, “Deep learning based classification of malaria from slide images,” Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT), pp. 1-4, 2019. DOI:

Z. Liang et al., "CNN-based image analysis for malaria diagnosis," Proc. - 2016 IEEE Int. Conf. Bioinforma. Biomed. BIBM 2016, pp. 493–496, 2017, doi: 10.1109/BIBM.2016.7822567. DOI:

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

K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," Sep. 2015, Accessed: May 11, 2021. [Online]. Available:

Y. Dong et al., "Evaluations of deep convolutional neural networks for automatic identification of malaria infected cells," 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), 2017, pp. 101-104, doi: 10.1109/BHI.2017.7897215. DOI:

Abdulghany, Eman & Osama, Nada. (2021). “Classification of Malaria Cell Images with Deep Learning Architectures”. 10.13140/RG.2.2.26387.40484.

Marada Amrutha Reddy, Ganti Sai Siva Rama Krishna, Teki Tanoj Kumar, 2021, “Malaria Cell-Image Classification using InceptionV3 and SVM”, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 10, Issue 08 (August 2021).

A. S. B. Reddy and D. S. Juliet, "Transfer Learning with ResNet 50 for Malaria Cell-Image Classification," 2019 International Conference on Communication and Signal Processing (ICCSP), 2019, pp. 0945-0949, doi: 10.1109/ICCSP.2019.8697909. DOI:

R. Agarwal, A. S. Sathwik, D. Godavarthi, and J. V. Naga Ramesh, “Comparative Analysis of Deep Learning Models for Multiclass Alzheimer’s Disease Classification”, EAI Endorsed Trans Perv Health Tech, vol. 9, Nov. 2023. DOI: sets.html.

R. Agarwal, J. Suthar, S. K. Panda, and S. N. Mohanty, “Fuzzy and Machine Learning based Multi-Criteria Decision Making for Selecting Electronics Product”, EAI Endorsed Scal Inf Syst, vol. 10, no. 5, Jul. 2023. DOI:

Rahman, Aimon & Zunair, Hasib & Reme, Tamanna & Rahman, Mohammad & Mahdy, Mahdy Rahman Chowdhury. (2020). A Comparative Analysis of Deep Learning Architectures on High Variation Malaria Parasite Classification Dataset. Tissue and Cell. 69. 101473. 10.1016/j.tice.2020.101473. DOI:

Jasman P., Irma Amelia, Reza & Yani. Automated Malaria Diagnosis Using Object Detection Retina-Net Based on Thin Blood Smear Images. ISSN:1992-8645.

Parveen, Rahila & Song, Wei & Qiu, Baozhi & Bhatti, Mairaj & Hassan, Tallal & Liu, Ziyi. (2021). Probabilistic Model-Based Malaria Disease Recognition System. Complexity. 2021. 1-11. 10.1155/2021/6633806. DOI:

Mehanian C, Jaiswal M, Delahunt C, Thompson C, Horning M, Hu L, Ostbye T, McGuire S, Mehanian M, Champlin C, Wilson B. Computer-automated malaria diagnosis and quantitation using convolutional neural networks. InProceedings of the IEEE International Conference on Computer Vision 2017 (pp. 116-125). DOI:

Bibin, Dhanya & S. Nair, Madhu & Punitha, P.. (2017). Malaria Parasite Detection From Peripheral Blood Smear Images Using Deep Belief Networks. IEEE Access. 10.1109/ACCESS.2017.2705642. DOI:




How to Cite

Naga Ramesh JV, Agarwal R, Jyasta H, Sivani B, Sri Tulasi Mounika PA, Bhargavi B. Automated Life Stage Classification of Malaria Using Deep Learning. EAI Endorsed Trans Perv Health Tech [Internet]. 2024 Mar. 15 [cited 2024 Apr. 25];10. Available from: