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

Authors

DOI:

https://doi.org/10.4108/eetpht.10.5551

Keywords:

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

Abstract

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.

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References

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Published

26-03-2024

How to Cite

1.
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: https://publications.eai.eu/index.php/phat/article/view/5551

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