Developing an Automated Decision-Supporting System to Diagnose Malaria Parasite from Thin Blood Smear Images Using Deep Neural Networks

Abstract

Malaria is a life-threatening disease caused by a parasite called Plasmodium, which is transmitted to humans through the bite of infected female Anopheles mosquitoes. The accurate detection of malaria parasite from thin blood smear images is imperative to improve diagnosis.

Purpose: This study aims to investigate the use of a deep neural networks for developing automated clinical decision-making system to improve malaria detection and evaluate its performance.

Materials and Methods: A deep neural network was proposed and trained on a dataset of microscopic images of thin blood smears to detect the presence of the malaria parasite. The collection of data comprises 27,558 pictures of red blood cells, where the number of infected and uninfected cells is equal. To test the model's effectiveness, a subset of 2000 images was taken, and the accuracy, precision, recall, and f1-score were used as performance indicators.

Results: The results showed that the proposed deep neural network achieved an accuracy of 0.96, indicating its effectiveness in detecting the disease. The precision score of 0.98 indicates that the model has a low rate of false positives, while the recall score of 0.947 indicates that it can detect most cases of malaria. The f1-score of 0.96 shows a good balance between precision and recall.

Conclusion: The study demonstrates the potential of using a deep neural network for accurate and efficient malaria detection. The high accuracy, precision, and recall scores suggest that the model is effective in detecting the disease and can minimize the risk of misdiagnosis. Therefore, the proposed deep neural network approach can serve as a promising tool for malaria diagnosis and control.