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Weighted ensemble CNN for lung nodule classification: an evolutionary approach

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Abstract

Lung Cancer is the deadliest cancer with maximum mortality rates all over the world. If lung cancers are not detected at an early stage, they will continue to be the leading cause of death due to cancer. Convolution neural networks (CNNs), a type of deep learning approach, have produced better outcomes for diagnosing malignant lung nodules. However, CNN can have variance in output which can be reduced using the ensemble learning technique. CNN ensemble approach also reduces the generalization error and increases the accuracy of lung nodule malignancy prediction using the ensemble CNN learning technique. In this study we reduce the variance in output and also improve the performance of lung nodule classification by using the weighted average ensemble technique on convolution models such as VGG16, Densenet, and Alexnet, and the weights are optimized using an evolutionary algorithm like grey wolf optimizer, whale swarm optimizer, firefly optimizer, differential evolution, particle swarm optimizer, and bat optimizer. The proposed approach uses a heterogeneous ensemble with a weighted approach and is evaluated on the LUNA 16 dataset, on which it has achieved an accuracy, sensitivity, and specificity of 96%, 97.43%, and 93.08% respectively, with an AUC score of 0.95 using an evolutionary algorithm like Bat Optimizer to optimize the weights.

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Data availability

The datasets generated and analyzed during the current study are available in the LUNA 16 repository.

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The authors state that they have no known conflicting financial interests or personal connections that could have appeared to affect the work presented in this publication.

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Authors and Affiliations

  1. Department of Computer Engineering, Don Bosco College of Engineering, Margao, Goa, India

    Amrita Naik

  2. Department of Computer Science & Engineering, National Institute of Technology, Ponda, Goa, India

    Damodar Reddy Edla & Saidi Reddy Parne

  3. Department of Computer Science & Engineering, Kakatiya Institute of Technology and Science, Warangal, Telangana, India

    Hanumanthu Bhukya

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  1. Amrita Naik
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Naik, A., Edla, D.R., Parne, S.R. et al. Weighted ensemble CNN for lung nodule classification: an evolutionary approach. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-023-17996-x

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