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Breast cancer classification using snapshot ensemble deep learning model and t-distributed stochastic neighbor embedding

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Abstract

The current work aims to analyse the historical data pertaining to breast cancer to detect and predict the disease. For the same, authors employ t-distributed stochastic neighbor embedding (t-SNE), a well-established dimensionality reduction method. The authors also suggest implementing the snapshot ensembling technique to create an efficient model that potentially assists medical professionals in disease diagnosis. Employing t-SNE enables the generation of improved scatter plots in addition to cost optimization. Further, the current manuscript also uses a snapshot ensemble deep learning framework that integrates the predictions through various base models leading to accuracy enhancement. The proposed model is implemented on the Wisconsin Breast Cancer Dataset(WBCD) that is openly accessible at UCI Machine Repository. During the experimental evaluation, proposed model yields an accuracy of 86.6%,higher than the state-of-art models like averaging, weighted averaging, stacked ensemble, and Polyak Rupert that yield an accuracy of 81%, 81.7%, 84.7%, and 82.2% respectively and hence establishes the competence of proposed model. The obtained result is highly encouraging and resultantly opens the avenue for implementing the proposed model in real life at large.

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Authors and Affiliations

  1. Indira Gandhi Delhi Technical University for Women, Delhi, India

    Nonita Sharma

  2. Dr BR Ambedkar National Institute of Technology, Jalandhar, Punjab, India

    K. P. Sharma & Rajneesh Rani

  3. Dwarkadas J. Sanghvi College of Engineering, Mumbai, India

    Monika Mangla

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  1. Nonita Sharma
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  2. K. P. Sharma
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  4. Rajneesh Rani
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Correspondence to Nonita Sharma.

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Sharma, N., Sharma, K.P., Mangla, M. et al. Breast cancer classification using snapshot ensemble deep learning model and t-distributed stochastic neighbor embedding. Multimed Tools Appl 82, 4011–4029 (2023). https://doi.org/10.1007/s11042-022-13419-5

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