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Segmentation and classification of brain tumors using modified median noise filter and deep learning approaches

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Abstract

The most vital challenge for a radiologist is locating the brain tumors in the earlier stage. As the brain tumor grows rapidly, doubling its actual size in about twenty-five days. If not dealt properly, the affected person’s survival rate usually does no longer exceed half a year. This can rapidly cause dying. For this reason, an automatic system is desirable for locating brain tumors at the early stage. In general, when compared to computed tomography (CT), magnetic resonance image (MRI) scans are used for detecting the diagnosis of cancerous and noncancerous tumors. However, while MRI scans acquisition, there is a chance of appearing noise such as speckle noise, salt & pepper noise and Gaussian noise. It may degrade classification performance. Hence, a new noise removal algorithm is proposed, namely the modified iterative grouping median filter. Further, Maximum likelihood estimation-based kernel principal component analysis is proposed for feature extraction. A deep learning-based VGG16 architecture has been utilized for segmentation purposes. Experimental results have shown that the proposed algorithm outperforms the well-known techniques in terms of both qualitative and quantitative evaluation.

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Author notes

  1. S. Ramesh, S. Sasikala and Nirmala Paramanandham contributed equally to this work.

Authors and Affiliations

  1. Vignan Foundation for Science, Technology & Research, Guntur, Andhra Pradesh, India

    S. Ramesh

  2. Department of ECE, Kongu Engineering College, Erode, Tamilnadu, India

    S. Sasikala

  3. School of Electronics Engineering, Vellore Institute of Technology, Chennai, Tamilnadu, India

    Nirmala Paramanandham

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  1. S. Ramesh
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Correspondence to Nirmala Paramanandham.

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Ramesh, S., Sasikala, S. & Paramanandham, N. Segmentation and classification of brain tumors using modified median noise filter and deep learning approaches. Multimed Tools Appl 80, 11789–11813 (2021). https://doi.org/10.1007/s11042-020-10351-4

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