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MRI-based brain tumor detection using the fusion of histogram oriented gradients and neural features

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Abstract

Computer-aided diagnoses are playing a remarkable role in analyzing the MRI images and thus assist the radiologist. The brain tumor is one of the most common and incursive diseases, leading to a very short life expectancy in their highest grade. Early detection of the brain tumor and its proper diagnosis may increase the survival rate of a patient. This paper has developed an intelligent system based on the fusion of histogram of oriented gradient and deep CNN-based neural features from MRI in the identification of tumors. This research has performed Principle Component Analysis as a feature optimization technique to attain more intuitive features from the fused feature vector. A machine learning-based ensemble classifier based on bagging and boosting known as Bootstrap Aggregation is then used on the optimized fusion vector to detect the tumor. An extensive experiment is performed using four types of MRI sequences T1, T2, T1c, and Flair from two publicly available datasets BRATS 2013 and BRATS 2015. This fusion-based method gives a satisfactory performance with a detection accuracy of 98.79% using a fivefold cross-validation technique.

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Data availability

The datasets used in the paper are publicly available. Proper acknowledgments with citation guidelines are maintained for the use of these datasets.

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Acknowledgements

We are very grateful to Dr. Md. Farhan Matin, Associate Professor, Department of Radiology and Imaging, Uttara Adhunik Medical College & Hospital, Dhaka, Bangladesh for his valuable suggestions.

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No funding was received for this research.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh

    Rafid Mostafiz, Nur-A Alam, Md. Mahmodul Hasan & Mohammad Motiur Rahman

  2. Department of Computer Science and Engineering, Dhaka International University, Dhaka, Bangladesh

    Rafid Mostafiz & Nur-A Alam

  3. Department of Computer Science and Engineering, Jahangirnagar University, Dhaka, Bangladesh

    Mohammad Shorif Uddin

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  1. Rafid Mostafiz
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  2. Mohammad Shorif Uddin
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  3. Nur-A Alam
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  5. Mohammad Motiur Rahman
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Correspondence to Mohammad Shorif Uddin.

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The authors declare that the research was conducted in the absence of any commercial or financial relationships. Therefore, there are no competing interests.

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Mostafiz, R., Uddin, M.S., Alam, NA. et al. MRI-based brain tumor detection using the fusion of histogram oriented gradients and neural features. Evol. Intel. 14, 1075–1087 (2021). https://doi.org/10.1007/s12065-020-00550-1

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  • DOI: https://doi.org/10.1007/s12065-020-00550-1

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