Convolutional Neural Networks in Malaria Diagnosis: A Study on Cell Image Classification




Malaria, ResNet50, AlexNet, Inception V3, VGG19, VGG16, precision, recall, F1-score, deep learning


INTRODUCTION: Malaria, a persistent global health threat caused by Plasmodium parasites, necessitates rapid and accurate identification for effective treatment and containment. This study investigates the utilization of convolutional neural networks (CNNs) to enhance the precision and speed of malaria detection through the classification of cell images infected with malaria.

OBJECTIVES: The primary objective of this research is to explore the effectiveness of CNNs in accurately classifying malaria-infected cell images. By employing various deep learning models, including ResNet50, AlexNet, Inception V3, VGG19, VGG16, and MobileNetV2, the study aims to assess the performance of each model and identify their strengths and weaknesses in malaria diagnosis.

METHODS: A balanced dataset comprising approximately 8,000 enhanced images of blood cells, evenly distributed between infected and uninfected classes, was utilized for model training and evaluation. Performance evaluation metrics such as precision, recall, F1-score, and accuracy were employed to assess the efficacy of each CNN model in malaria classification.

RESULTS: The results demonstrate high accuracy across all models, with AlexNet and VGG19 exhibiting the highest levels of accuracy. However, the selection of a model should consider specific application requirements and constraints, as each model presents unique trade-offs between computational efficiency and performance.

CONCLUSION: This study contributes to the burgeoning field of deep learning in healthcare, particularly in utilizing medical imaging for disease diagnosis. The findings underscore the considerable potential of CNNs in enhancing malaria diagnosis. Future research directions may involve further model optimization, exploration of larger and more diverse datasets, and the integration of CNNs into practical diagnostic tools for real-world deployment.


Download data is not yet available.


Jünger, S. T., Hoyer, U. C. I., Schaufler, D., Laukamp, K. R., Goertz, L., Thiele, F., Grunz, J., Schlamann, M., Perkuhn, M., Kabbasch, C., Persigehl, T., Grau, S., Borggrefe, J., Scheffler, M., Shahzad, R., & Pennig, L. (2021). Fully Automated MR Detection and Segmentation of Brain Metastases in Non‐small Cell Lung Cancer Using Deep Learning. Journal of Magnetic Resonance Imaging, 54(5), 1608–1622. DOI:

Masud, M., Sikder, N., Nahid, A.-A., Bairagi, A. K., & AlZain, M. A. (2021). A Machine Learning Approach to Diagnosing Lung and Colon Cancer Using a Deep Learning-Based Classification Framework. Sensors (Basel, Switzerland), 21(3), 748. DOI:

Coudray, N., Ocampo, P. S., Sakellaropoulos, T., Narula, N., Snuderl, M., Fenyö, D., Moreira, A. L., Razavian, N., & Tsirigos, A. (2018). Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nature Medicine, 24(10), 1559–1567. DOI:

Chen, J., Zeng, H., Zhang, C., Shi, Z., Dekker, A., Wee, L., & Bermejo, I. (2022). Lung cancer diagnosis using deep attention-based multiple instance learning and radiomics. Medical Physics (Lancaster), 49(5), 3134–3143. DOI:

Yeh, M. C.-H., Wang, Y.-H., Yang, H.-C., Bai, K.-J., Wang, H.-H., & Li, Y.-C. (2021). Artificial Intelligence-Based Prediction of Lung Cancer Risk Using Nonimaging Electronic Medical Records: Deep Learning Approach. Journal of Medical Internet Research, 23(8), e26256–e26256. DOI:

Rong, Z., Lingyun, D., Jinxing, L., & Ying, G. (2021). Diagnostic Classification of Lung Cancer Using Deep Transfer Learning Technology and Multi‐Omics Data. CHINESE JOURNAL OF ELECTRONICS, 30(5), 843–852. DOI:

Tan, H., Bates, J. H. T., & Matthew Kinsey, C. (2022). Discriminating TB lung nodules from early lung cancers using deep learning. BMC Medical Informatics and Decision Making, 22(1), 1–161. DOI:

Nishio, M., Sugiyama, O., Yakami, M., Ueno, S., Kubo, T., Kuroda, T., & Togashi, K. (2018). Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neural network with transfer learning. PloS One, 13(7), e0200721–e0200721. DOI:

Park, J., Kang, S. K., Hwang, D., Choi, H., Ha, S., Seo, J. M., Eo, J. S., & Lee, J. S. (2023). Automatic Lung Cancer Segmentation in [18F]FDG PET/CT Using a Two-Stage Deep Learning Approach. Nuclear Medicine and Molecular Imaging, 57(2), 86–93. DOI:

Talukder, M. A., Islam, M. M., Uddin, M. A., Akhter, A., Hasan, K. F., & Moni, M. A. (2022). Machine learning-based lung and colon cancer detection using deep feature extraction and ensemble learning. Expert Systems with Applications, 205, 117695. DOI:

Zhang, H., Liao, M., Guo, Q., Chen, J., Wang, S., Liu, S., & Xiao, F. (2023). Predicting N2 lymph node metastasis in presurgical stage I‐II non‐small cell lung cancer using multiview radiomics and deep learning method. Medical Physics (Lancaster), 50(4), 2049–2060. DOI:

Li, B., Dai, C., Wang, L., Deng, H., Li, Y., Guan, Z., & Ni, H. (2020). A novel drug repurposing approach for non-small cell lung cancer using deep learning. PloS One, 15(6), e0233112–e0233112. DOI:

Zheng, S., Guo, J., Langendijk, J. A., Both, S., Veldhuis, R. N. J., Oudkerk, M., van Ooijen, P. M. A., Wijsman, R., & Sijtsema, N. M. (2023). Survival prediction for stage I-IIIA non-small cell lung cancer using deep learning. Radiotherapy and Oncology, 180, 109483. DOI:

Patel, B. N., & Langlotz, C. P. (2021). Beyond the AJR: "Deep Learning Using Chest Radiographs to Identify High- Risk Smokers for Lung Cancer Screening Computed Tomography: Development and Validation of a Prediction Model" American Journal of Roentgenology (1976), 217(2), 521–521. DOI:

Varchagall, M., Nethravathi, N. P., Chandramma, R., Nagashree, N., & Athreya, S. M. (2023). Using Deep Learning Techniques to Evaluate Lung Cancer Using CT Images. SN Computer Science, 4(2). DOI:

Chen, C.-L., Chen, C.-C., Yu, W.-H., Chen, S.-H., Chang, Y.-C., Hsu, T.-I., Hsiao, M., Yeh, C.-Y., & Chen, C.-Y. (2021). An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning. Nature Communications, 12(1), 1193–1193. DOI:

Torres, F. S., Akbar, S., Raman, S., Yasufuku, K., Hannessy, T. J., Baldauf-Lenschen, F., & Leighl, N. B. (2022). Automated imaging-based prognostication (IPRO) for stage I non-small cell lung cancer using deep learning applied to computed tomography. Journal of Clinical Oncology, 40(16_suppl), e20575–e20575. DOI:

G. P. Rout and S. N. Mohanty, "A Hybrid Approach for Network Intrusion Detection," 2015 Fifth International Conference on Communication Systems and Network Technologies, Gwalior, India, 2015, pp. 614-617, doi: 10.1109/CSNT.2015.76. DOI:

Alenezi, F.; Armghan, A.; Mohanty, S.N.; Jhaveri, R.H.; Tiwari, P. Block-Greedy and CNN Based Underwater Image Dehazing for Novel Depth Estimation and Optimal Ambient Light. Water 2021, 13, 3470. DOI:




How to Cite

Ghosh H, Rahat IS, Ravindra JVR, J B, Ullah Khan MA, Somasekar J. Convolutional Neural Networks in Malaria Diagnosis: A Study on Cell Image Classification. EAI Endorsed Trans Perv Health Tech [Internet]. 2024 Mar. 26 [cited 2024 Apr. 21];10. Available from:

Most read articles by the same author(s)