Identification and Delineation of Acquired Brain Anomalies Through Neural Network Classification Technique
Krishnakumar S
School of Electrical and Electronics Engineering, Faculty of Engineering and Technology at SRM Institute of Science and Technology, Tiruchirappalli, Tamil Nadu, India.
Department of Electronics and Communication Engineering, Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College), Tirupati, Andra Pradesh, India.
Acquired brain anomalies are crucial and life killing disease among the other diseases. As a result, fast and accurate disease diagnosis and classification are critical for human survival. In this research, a machine learning strategy is given for distinguishing and classifying meningioma brain images from non-meningioma brain images. In this paper, brain pictures are recognized and classified using the Neural Network (NN) classification method. This suggested method comprises of a preprocessing module that uses the shearlet transform for transformation of pixels. Local Binary Pattern (LBP) features are then calculated using the shearlet coefficients. The computed final characteristics are input into the NN classifier to produce classification results. The meningioma detection system using the suggested NN classification approach obtains 96.45% of SET, 96.57% of SPT, 97.34% of MSA, 97.38% of PR, and 97.3% of FS. The meningioma detection system using the suggested NN classification approach obtains 97.16% of SET, 97.25% of SPT, 97.97% of MSA, 98.19% of PR, and 98. The shearlet transform combined with NN classification algorithm improves the performance of the entire meningioma detection rate.
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CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Krishnakumar S, Satya Srinivas Maddipati K, Deepika Attavar, Ashokkumar N, Karthikeyan S and Amaravathi D;
Methodology: Krishnakumar S and Satya Srinivas Maddipati K;
Visualization: Karthikeyan S and Amaravathi D;
Investigation: Krishnakumar S and Satya Srinivas Maddipati K;
Supervision: Karthikeyan S and Amaravathi D;
Validation: Ashokkumar N and Karthikeyan S;
Writing- Reviewing and Editing: Krishnakumar S, Satya Srinivas Maddipati K, Deepika Attavar, Ashokkumar N, Karthikeyan S and Amaravathi D; All authors reviewed the results and approved the final version of the manuscript.
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The authors would like to thank to the reviewers for nice comments on the manuscript.
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Krishnakumar S
School of Electrical and Electronics Engineering, Faculty of Engineering and Technology at SRM Institute of Science and Technology, Tiruchirappalli, Tamil Nadu, India.
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Cite this article
Krishnakumar S, Satya Srinivas Maddipati K, Deepika Attavar, Ashokkumar N, Karthikeyan S and Amaravathi D, “Identification and Delineation of Acquired Brain Anomalies Through Neural Network Classification Technique”, Journal of Machine and Computing, vol.5, no.3, pp. 1450-1458, July 2025, doi: 10.53759/7669/jmc202505115.
