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A Feature Extraction Using Probabilistic Neural Network and BTFSC-Net Model with Deep Learning for Brain Tumor

Arun Singh Yadav¹, Surendra Kumar², Girija Rani Karetla³

¹Department of Computer Science, University of Lucknow, Lucknow 226007, Uttar Pradesh, India.

Journal of Imaging

|January 20, 2023

Summary

This study introduces Brain Tumor Fusion-based Segments and Classification-Non-enhancing tumor (BTFSC-Net), a novel system for brain tumor classification. The BTFSC-Net achieved high accuracy in image segmentation and tumor classification, outperforming traditional methods.

Keywords:

DLPNN brain tumor segmentation classification deep learning feature extraction robust edge analysis

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Area of Science:

Medical Imaging Analysis
Artificial Intelligence in Healthcare
Computational Pathology

Background:

Brain tumors require accurate segmentation and classification for effective treatment.
Existing methods for brain tumor analysis often face challenges with noise, feature extraction, and classification accuracy.

Purpose of the Study:

To develop a hybrid system, Brain Tumor Fusion-based Segments and Classification-Non-enhancing tumor (BTFSC-Net), for enhanced brain tumor classification.
To integrate advanced techniques for image preprocessing, fusion, segmentation, feature extraction, and classification.

Main Methods:

Applied Hybrid Probabilistic Wiener Filter (HPWF) for noise reduction.
Utilized deep learning convolutional neural networks (DLCNN) for image fusion incorporating Robust Edge Analysis (REA).
Employed Adaptive Fuzzy C-Means integrated K-Means (HFCMIK) for segmentation and extracted features using Redundant Discrete Wavelet Transform (RDWT), empirical color, and Gray-Level Co-occurrence Matrix (GLCM).
Deployed a Deep Learning Probabilistic Neural Network (DLPNN) for final tumor classification.

Main Results:

The proposed BTFSC-Net model demonstrated superior performance compared to traditional techniques.
Achieved 99.21% accuracy in image segmentation.
Reached 99.46% accuracy in brain tumor classification.

Conclusions:

The BTFSC-Net system significantly outperforms previous approaches in brain tumor image fusion, segmentation, feature extraction, and classification.
The developed method offers enhanced quantitative and visual performance for brain tumor analysis.