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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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Brain Tumor Detection Based on Hybrid Convolutional Adaptive Neuro Fuzzy Inference System Using MRI Image.

Sridhar S R1, Akila M2, Asokan R3

  • 1Department of Computer Science and Engineering, Muthyammal Engineering College, Namakkal, Tamil Nadu, India.

NMR in Biomedicine
|September 4, 2025
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Summary

This study introduces a novel Convolutional-Adaptive Neuro-Fuzzy Inference System (Conv-ANFIS) for accurate brain tumor detection in MRI scans. The Conv-ANFIS model significantly improves detection rates compared to existing methods.

Keywords:
convolutional neural networkconvolutional‐adaptive neuro‐fuzzy inference systemfeature vectorgrey level co‐occurrence matrixnon‐local means filterstructure correcting adversarial network

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Neuroscience

Background:

  • Brain tumors are life-threatening, and their early detection is crucial.
  • Current magnetic resonance imaging (MRI) detection methods face challenges with noise, segmentation accuracy, and generalization.
  • Limitations in existing techniques necessitate advanced approaches for reliable brain tumor identification.

Purpose of the Study:

  • To develop and evaluate a novel Convolutional-Adaptive Neuro-Fuzzy Inference System (Conv-ANFIS) for enhanced brain tumor detection from MRI images.
  • To address the limitations of existing brain tumor detection methods, including noise handling and segmentation accuracy.
  • To improve the overall accuracy and reliability of brain tumor identification using a hybrid AI model.

Main Methods:

  • A Convolutional Neural Network (CNN) is integrated with an Adaptive Neuro-Fuzzy Inference System (ANFIS) to create the Conv-ANFIS model.
  • Pre-processing steps include Non-Local Means (NLM) filtering for de-noising and skull removal.
  • Segmentation is performed using the Structure Correcting Adversarial Network (SCAN), followed by feature extraction and tumor identification via Conv-ANFIS.

Main Results:

  • The Conv-ANFIS approach achieved a recall of 92.31%, precision of 90.13%, and an F1-score of 91.21%.
  • The proposed method demonstrated superior performance compared to existing brain tumor detection techniques.
  • Effective de-noising, skull removal, and segmentation were achieved, leading to improved detection accuracy.

Conclusions:

  • The Conv-ANFIS model offers a robust and accurate solution for brain tumor detection in MRI images.
  • This hybrid AI approach effectively overcomes the limitations of traditional detection methods.
  • The study highlights the potential of integrating deep learning and fuzzy inference systems for advanced medical image analysis.