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The proposition of Possibilistic sigmoid features and the Shannon-Hanman transform classifier along with the pervasive learning model for the classification of brain tumor using MRI

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Abstract

As part of developing a computer-aided diagnosis system for the early detection/classification of brain tumors, this paper presents an Information set-based sigmoid features and a classifier using MR images. A set of information values constituting an Information set springs forth on fitting a membership function to a set of information source (attribute) values, the sum of which gives the certainty/uncertainty in the attribute values to a class, say, the pixel intensities in an MRI to a disease class. This certainty/uncertainty representation is not attempted in the existing methods, thus failing to produce efficient features. To this end, Hanman-Anirban (HA), Mamta-Hanman (MH), and Possibilistic Renyi entropy functions are employed including the pervasive membership function in the generation of four types of sigmoid features. The pervasive Information set results from the use of pervasive membership function that is a combination of the membership function and non-membership function. Furthermore, the Shannon-Hanman Transform classifier is formulated using the t-norm of error vectors between the training and test feature vectors, and its parameters are learned through the Pervasive learning model. The proposed system comprising features, classifier, and the learning model is tested on two Brain MRI’s datasets. The t-norm based fusion of two features has also been experimented. The Shannon-Hanman Transform classifier along with the Pervasive learning model is found to outperform the other classifiers in the literature with the highest accuracy of 99.51% for the two-class classification with a fusion of two features and 99.09% for the three-class classification with a sigmoid MH feature.

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Authors and Affiliations

  1. EECE Department, The NorthCap University, Gurugram, Haryana, India

    Pallavi Asthana & Sharda Vashisth

  2. CSE Department, MVSR Engineering College, Nadergul, Hyderabad, India

    Madasu Hanmandlu

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  1. Pallavi Asthana
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  2. Madasu Hanmandlu
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Correspondence to Pallavi Asthana.

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Asthana, P., Hanmandlu, M. & Vashisth, S. The proposition of Possibilistic sigmoid features and the Shannon-Hanman transform classifier along with the pervasive learning model for the classification of brain tumor using MRI. Multimed Tools Appl 81, 23913–23939 (2022). https://doi.org/10.1007/s11042-022-12482-2

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