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Discerning Monkeypox from Other Viruses of the Poxviridae Family in a Deep Learning Paradigm

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Proceedings of Data Analytics and Management (ICDAM 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 787))

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Abstract

The world suffered a lot due to the Covid outbreak of 2019 which resulted into a pandemic and millions of people losing their lives and livelihoods as its repercussions. While the world was still recovering from its repercussions, the cases of monkeypox arose and were very evident in the US, Europe and Africa as well. The early detection of a disease plays a very vital role in curbing its spread. Foreseeing the Covid outbreak, in its early stages, its detection was very time-taking and hence late detection resulted in the spread of the disease. Therefore, we propose a CNN-based ensemble which exploits the feature extraction capabilities of VGG-16, MobileNet-50, Inception-V3 and ResNet-50 architectures. We thereby achieve a better ensemble accuracy of 90% using a large dataset. Along with the accuracy, we also aim at improving the recall, precision and f1-score in our ensemble learning method. We treat this problem to be a multiclass classification problem since detection of chickenpox, measles, cowpox, smallpox and healthy skin images can be often confusing and overlapping.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Delhi Technological University (DTU), Delhi, 110042, India

    Malti Bansal, Riyanshi Arora, Sai Keshari & Sakshi Panchal

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  1. Malti Bansal
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  2. Riyanshi Arora
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  3. Sai Keshari
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  4. Sakshi Panchal
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Correspondence to Malti Bansal .

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Editors and Affiliations

  1. Department of Information Technology, Bhagwan Parshuram Institute of Technology, New Delhi, Delhi, India

    Abhishek Swaroop

  2. Jan Wyzykowski University, Polkowice, Poland

    Zdzislaw Polkowski

  3. Polytechnic Institute of Portalegre, Portalegre, Portugal

    Sérgio Duarte Correia

  4. Centre for Communications Technology, London Metropolitan university, London, UK

    Bal Virdee

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Bansal, M., Arora, R., Keshari, S., Panchal, S. (2023). Discerning Monkeypox from Other Viruses of the Poxviridae Family in a Deep Learning Paradigm. In: Swaroop, A., Polkowski, Z., Correia, S.D., Virdee, B. (eds) Proceedings of Data Analytics and Management. ICDAM 2023. Lecture Notes in Networks and Systems, vol 787. Springer, Singapore. https://doi.org/10.1007/978-981-99-6550-2_3

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