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Classification of brain MR images using Modified version of Simplified Pulse-Coupled Neural Network and Linear Programming Twin Support Vector Machines

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Abstract

The automated and accurate detection of brain tumors is challenging for classifying brain Magnetic Resonance (MR) images. The conventional techniques for diagnosing the images are tedious and inefficient in decision making. Therefore, this work proposes an adaptive and non-invasive method for accurately classifying images into pathological and normal brain MR images to overcome these drawbacks. This system uses the Skull Stripping algorithm for removing the non-cerebral tissues. We have developed the Modified version of Simplified Pulse-Coupled Neural Network for segmenting the Region of Interest. The Stationary Wavelet Transform is employed for transforming the image to extract the multiresolution data from the segmented images. The dimensionality of the transformed images is high. Thus, texture- and intensity-based features are extracted from transformed images, and the features of least entropy are selected to make a set of prominent features. Finally, Probabilistic Neural Network and Linear Programming Twin Support Vector Machines with Newton-Armijo algorithm are applied for the classification of images. The validation of the experiments is carried out on the three databases, viz., DB-66, DB-160, and DB-255. The experimental results show that the suggested scheme is robust and effective as compared to other state-of-the-art schemes. The suggested method can assist the radiologists in treatment planning. Hence, the proposed method can effectively classify the brain MR images and be installed on medical machines.

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  1. ABV-Indian Institute of Information Technology and Management, Morena Link Road, Gwalior, 474015, India

    Ravi Shanker & Mahua Bhattacharya

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  1. Ravi Shanker
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  2. Mahua Bhattacharya
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Correspondence to Ravi Shanker.

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Shanker, R., Bhattacharya, M. Classification of brain MR images using Modified version of Simplified Pulse-Coupled Neural Network and Linear Programming Twin Support Vector Machines. J Supercomput 78, 13831–13863 (2022). https://doi.org/10.1007/s11227-022-04420-8

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