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Hybrid Optimization Algorithm Enabled Deep Learning Approach Brain Tumor Segmentation and Classification Using MRI

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Abstract

The unnatural and uncontrolled increase of brain cells is called brain tumors, leading to human health danger. Magnetic resonance imaging (MRI) is widely applied for classifying and detecting brain tumors, due to its better resolution. In general, medical specialists require more details regarding the size, type, and changes in small lesions for effective classification. The timely and exact diagnosis plays a major role in the efficient treatment of patients. Therefore, in this research, an efficient hybrid optimization algorithm is implemented for brain tumor segmentation and classification. The convolutional neural network (CNN) features are extracted to perform a better classification. The classification is performed by considering the extracted features as the input of the deep residual network (DRN), in which the training is performed using the proposed chronological Jaya honey badger algorithm (CJHBA). The proposed CJHBA is the integration of the Jaya algorithm, honey badger algorithm (HBA), and chronological concept. The performance is evaluated using the BRATS 2018 and Figshare datasets, in which the maximum accuracy, sensitivity, and specificity are attained using the BRATS dataset with values 0.9210, 0.9313, and 0.9284, respectively.

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Data Availability

The data underlying this article are available in BRATS 2018 dataset at https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=37224922 and Figshare dataset at https://figshare.com/articles/brain_tumor_dataset/1512427

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Acknowledgements

I would like to express my very great appreciation to the co-authors of this manuscript for their valuable and constructive suggestions during the planning and development of this research work.

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Authors and Affiliations

  1. Professor, Department of ECE, Panimalar Engineering College, Chennai, India

    S. Deepa

  2. Professor, Department of CSE, Sri Krishna College of Engineering and Technology, Coimbatore, India

    J. Janet & J. P. Ananth

  3. Professor, Department of EEE, Mahendra Engineering College, Namakkal, India

    S. Sumathi

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All authors have made substantial contributions to conception and design, revising the manuscript, and the final approval of the version to be published. Also, all authors agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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Correspondence to S. Deepa.

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Deepa, S., Janet, J., Sumathi, S. et al. Hybrid Optimization Algorithm Enabled Deep Learning Approach Brain Tumor Segmentation and Classification Using MRI. J Digit Imaging 36, 847–868 (2023). https://doi.org/10.1007/s10278-022-00752-2

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