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A Novel Shallow ConvNet-18 for Malaria Parasite Detection in Thin Blood Smear Images

CNN Based Malaria Parasite Detection

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Abstract

Accurate diagnosis of plasmodium parasite from blood cell images is essential to prevent the further spreading of the deadliest disease, malaria. It is an infectious disease, mainly transmitted by female Anopheles bite. Conventionally, microscopists can diagnose this disease by examining the thick and thin blood smears. Due to inter/intraobserver errors, the classification accuracy may get affected. To overcome this, a robust and shallow convolutional neural network is developed for the automatic detection of malaria parasite using thin blood smear images. The network is trained with 80% of images (11,023 parasitized and 11,023 uninfected) and tested with 20% (2756 parasitized and 2756 uninfected) of images. Several standard pre-trained models like Alexnet, VGG-16, VGG-19, Resnet-18, Resnet-50, Resnet-101, Squeezenet, Mobilenet-v2, Inception-v3, Googlenet, Xception, and Densenet-201 are implemented and the results obtained are compared with the proposed method. Classification accuracy, sensitivity, specificity, positive predictive value, and F1-score are the metrics used to evaluate the performance of the networks. Compared to existing pre-trained models, the proposed CNN has achieved better results with classification accuracy of 97.8%, sensitivity of 97.9%, specificity of 97.8%, positive predictive value of 97.8%, and F1-score of 97.84%. The proposed method performs the training much faster compared to the pre-trained network due to less number of parameters.

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Authors and Affiliations

  1. Department of ECE, National Institute of Technology Puducherry, Karaikal, India

    Poonguzhali Elangovan

  2. Department of ECE, National Institute of Technology Puducherry, Karaikal, India

    Malaya Kumar Nath

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  1. Poonguzhali Elangovan
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  2. Malaya Kumar Nath
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Correspondence to Poonguzhali Elangovan.

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Elangovan, P., Nath, M.K. A Novel Shallow ConvNet-18 for Malaria Parasite Detection in Thin Blood Smear Images. SN COMPUT. SCI. 2, 380 (2021). https://doi.org/10.1007/s42979-021-00763-w

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