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Brain tumor detection using deep ensemble model with wavelet features

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Abstract

Purpose

A person's healthy activities are determined by the state of his or her brain. The brain is in charge of all of a person's activities. If a small abnormality develops in the brain, it will have a negative impact on the person regardless of whether the other organs are in good condition. As a result, early detection of any abnormal growth in the brain is essential.

Methods

In this work, the authors have utilized data pre-processing using discrete wavelet transform (DWT) and segmentation, whereas, for detection, an ensemble learning technique is proposed. DWT and segmentation help in increasing the dataset size that is used to train the deep learning model. Segmentation using supervised Auto-encoder (AE) is used for data enhancement to strengthen the training process. The original data, outputs of DWT, and segmented images are utilized for the training of the ensemble model designed with three parallel-connected convolutional neural networks (CNNs).

Results

The detection results obtained from the ensemble of these recurrent models are then passed through the Multilayer Perceptron (MLP) for final detection. Kaggle brain MRI image dataset is used to complete the proposed method. Test accuracy, F1-score, precision, sensitivity, and specificity provided by this method are 98.08%, 0.9836, 1.0000, 0.9677, and 1.0000 respectively. In comparison to state-of-the-art models, the proposed model produces competitive outcomes.

Conclusion

In time detection of the tumor may lead to the survival of the patient. Automatic and accurate detection is another perspective of this field. For this purpose, we have proposed a deep ensemble model with wavelet features. The ensemble model provides increased performance in comparison to single models due to the parallel training.

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Availability of data and material

The data used in this work is cited.

Code availability

Not applicable.

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Funding

The authors did not receive support from any organization for the submitted work.

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Authors and Affiliations

  1. Centurion University of Technology and Management, Bhubaneswar, Odisha, India

    Debendra Kumar Sahoo & Satyasis Mishra

  2. ITER, Siksha ‘O’ Anusandhan (Deemed to Be University), Bhubaneswar, Odisha, India

    Abhishek Das & Mihir Narayan Mohanty

Authors
  1. Debendra Kumar Sahoo
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  2. Abhishek Das
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  3. Satyasis Mishra
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  4. Mihir Narayan Mohanty
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Contributions

D.K. Sahoo went through the literature survey, data collection and worked on designing the ensemble model, A. Das worked on the preprocessing steps and corrected the writing, S. Mishra and M.N. Mohanty formulated the problem and supervised the work.

Corresponding author

Correspondence to Mihir Narayan Mohanty.

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Sahoo, D.K., Das, A., Mishra, S. et al. Brain tumor detection using deep ensemble model with wavelet features. Health Technol. 12, 1157–1167 (2022). https://doi.org/10.1007/s12553-022-00699-y

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