Malaria Detection using Microscopic Image Analysis: A Convolution Neural Network Based Approach
Abstract
Malaria is a potentially fatal disease which is caused by Plasmodium parasite. These parasites are transmitted to humans through the bites of female Anopheles mosquitoes which play the role of disease vector. Five types of plasmodium cause malaria named P. Falciparum, P. Vivax, P. Ovale, P. Knowlesi, and P. Malariae, Among the Plasmodium parasites, Falciparum and Vivax are particularly lethal to humans. Therefore, early detection of malaria is mandatory to avoid the loss of human life. Different automatic/semi-automatic malaria detection techniques are available in the literature, which reduces the chance of human errors in the prognosis of malaria. In recent years, deep learning-based methods have proven to be effective for object detection. Therefore, such methods have caught the attention of researchers to use for the detection of malarial parasites in human blood. In this paper, we proposed a Convolutional Neural Network (CNN) model, which detects malarial parasites in microscopic images of human blood samples with high accuracy. The proposed model comprises 15 layers. It has 8 convolution layers with ReLu activation function, 4 max-pooling layers, 1 flattening layer, and 2 fully connected layers. The proposed method has been evaluated using various statistical measures against existing state-of-the-art methods. The quantitative measures show the effectiveness of the proposed model. It has a 97.42% testing accuracy, 97.42% sensitivity, 97.41% specificity, 97.70% precision, 97.42% recall , 97.97% F1-score , 97.41% Area Under Curve (AUC), and 94.82% Mathews correlation coefficient.
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