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Malaria Parasite Detection Using Deep Neural Networks

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Data Science and Applications (ICDSA 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 820))

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Abstract

Malaria, a contagious disease that has the potential to be fatal, affects millions of individuals globally. Early identification of malaria is one of the main difficulties in its efficient treatment. Microscopy is one of the older diagnostic techniques for malaria, although it has drawbacks such as limited sensitivity and specificity. Deep learning algorithms have recently demonstrated encouraging outcomes in the detection of malaria from photographs of infected and uninfected cells. To choose the most effective method for detecting malaria, we assess the performance of five alternative deep learning models in this study. The dataset used in this study contains many images of both infected and uninfected cells, the difference between these images is clearly visible when inspected visually. This research used the dataset from Kaggle that has two classes which are uninfected and parasitized images of blood cells. The study compared the performance of five different deep learning models in detecting malaria from these images: VGG-19, Inception-v3, ResNet-101, Inception Resnet-v2, and a custom CNN model. The results of the study indicated that Inception Resnet-v2 was the most accurate model, achieving an accuracy rate of 95.4%.

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Authors and Affiliations

  1. School of Computer Science and Artificial Intelligence, SR University, Warangal, Telangana, India

    Syed Asiya, Nagurla Mahender, Mohammed Raamizuddin & Perumalla Anoosha

  2. Balaji Institute of Technology and Science, Warangal, Telangana, India

    D. Aparna

Authors
  1. Syed Asiya
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  2. D. Aparna
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  3. Nagurla Mahender
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  4. Mohammed Raamizuddin
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  5. Perumalla Anoosha
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Corresponding author

Correspondence to Syed Asiya .

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Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Satyasai Jagannath Nanda

  2. Department of Electronics and Communication Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Rajendra Prasad Yadav

  3. Engineering & Information Technology, University of Technology Sydney, Ultimo, NSW, Australia

    Amir H. Gandomi

  4. Department of Computer Science and Engineering, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India

    Mukesh Saraswat

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Asiya, S., Aparna, D., Mahender, N., Raamizuddin, M., Anoosha, P. (2024). Malaria Parasite Detection Using Deep Neural Networks. In: Nanda, S.J., Yadav, R.P., Gandomi, A.H., Saraswat, M. (eds) Data Science and Applications. ICDSA 2023. Lecture Notes in Networks and Systems, vol 820. Springer, Singapore. https://doi.org/10.1007/978-981-99-7817-5_23

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