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Big data analytics enabled deep convolutional neural network for the diagnosis of cancer

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Abstract

Artificial intelligence (AI) has been shown to be a formidable instrument in managing Big Healthcare Data, and it has seen considerable success in bioinformatics. The advancement of big data in biological sciences has given rise to big data analytics (BDA) and artificial intelligence (AI). Because the AI methodologies used in bioinformatics are parallel and iterative, scalable big data management employing distributed and parallel technology is possible. The growth of bioinformatics has resulted in significant storage and administration issues; to share information, such large amounts of data must be handled efficiently. Computational developments in information technology have enabled analytical systems to cope with such data. Therefore, this study emphasizes the impact of big data and BDA in bioinformatics. A practical use of BDAs and AI in cancer classification was given, combining a unique Analysis of Variance (ANOVA) approach with Ant Colony Optimization (ACO) as a hybrid feature selection to pick significant genes while minimizing gene redundancy. Deep Convolutional Neural Networks (DCNN) were employed to classify the datasets. It is because microarray data are produced from gene expression data, and it frequently has a limited number of samples but a huge feature collection size. Using the same datasets, the suggested system outperformed earlier state-of-the-art approaches. The results of the proposed model on all the Leukemia, DLBCL, Colon, and SRDCT datasets revealed an average classification accuracy of 97.7%, 99.9%, 99.9% and 100%, respectively.

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Data availability

The dataset used in this paper is publicly available as follows: (a) Small Round Blue Cell Tumor (SRBCT) Dataset: https://rdrr.io/github/sarahromanes/multiDA/man/SRBCT.html. (b) Breast Cancer Dataset: https://arup.utah.edu/database/BRCA/, https://www.kaggle.com/datasets/uciml/breast-cancer-wisconsin-data. (c) Leukemia Dataset: https://www.openintro.org/data/index.php?data=golub, https://www.kaggle.com/datasets/crawford/gene-expression. (d) DLBCL Dataset: https://ecotyper.stanford.edu/lymphoma/. (e) Colon Cancer Dataset: http://biogps.org/dataset/tag/colon%20cancer/, https://www.kaggle.com/datasets/andrewmvd/lung-and-colon-cancer-histopathological-images.

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Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. Department of Computer Science, Faculty of Information and Communication Sciences, University of Ilorin, Ilorin, 240003, Kwara State, Nigeria

    Joseph Bamidele Awotunde

  2. Department of Computer Applications, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majitar, Sikkim, 737136, India

    Ranjit Panigrahi

  3. Electronics and Communication Engineering, KIET Group of Institutions, Delhi-NCR, Ghaziabad, 201206, India

    Shubham Shukla

  4. LTIMindtree, 1 American Row, 3Rd Floor, Hartford, CT, 06103, USA

    Baidyanath Panda

  5. Directorate of Research, Sikkim Manipal University, Gangtok, Sikkim, 737102, India

    Akash Kumar Bhoi

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  1. Joseph Bamidele Awotunde
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  2. Ranjit Panigrahi
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  3. Shubham Shukla
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  4. Baidyanath Panda
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All the authors have designed the study, developed the methodology, performed the analysis, and written the manuscript. All authors have contributed equally.

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Correspondence to Ranjit Panigrahi or Akash Kumar Bhoi.

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Awotunde, J.B., Panigrahi, R., Shukla, S. et al. Big data analytics enabled deep convolutional neural network for the diagnosis of cancer. Knowl Inf Syst 66, 905–931 (2024). https://doi.org/10.1007/s10115-023-01971-x

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