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Parallelistic Convolution Neural Network Approach for Brain Tumor Diagnosis.

Goodness Temofe Mgbejime1, Md Altab Hossin2, Grace Ugochi Nneji3,4

  • 1School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.

Diagnostics (Basel, Switzerland)
|October 27, 2022
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Summary
This summary is machine-generated.

This study introduces an automated method using Contrast Limited Adaptive Histogram Equalization (CLAHE) and a Pulse Coupled Neural Network (PCNN) for accurate brain tumor detection in Magnetic Resonance Imaging (MRI). The novel approach significantly enhances image quality and achieves high diagnostic accuracy.

Keywords:
MRIbrain tumordeep learningdisease diagnosismedical imaging

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • Magnetic Resonance Imaging (MRI) is crucial for identifying brain tumors, but manual analysis is time-consuming and requires expert interpretation.
  • Early detection of brain tumors improves patient outcomes, highlighting the need for automated diagnostic tools.
  • Low image quality in MRI, due to noise and artifacts, presents a significant challenge for accurate tumor detection.

Purpose of the Study:

  • To develop and evaluate an automated system for brain tumor detection and diagnosis using enhanced MRI images.
  • To address the limitations of manual analysis and improve the efficiency and accuracy of brain tumor identification.
  • To enhance the quality of low-resolution MRI images for better feature extraction and classification.

Main Methods:

  • A Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm was employed to preprocess MRI images, reducing noise and enhancing features.
  • A Pulse Coupled Neural Network (PCNN) was utilized for feature learning and tumor classification.
  • The model was trained and validated using a publicly available dataset, with experiments involving various optimizers, dropout rates, and learning rates.

Main Results:

  • The proposed PCNN model combined with CLAHE achieved high performance metrics: 98.7% accuracy, 99.7% sensitivity, and 97.4% specificity.
  • The automated system demonstrated superior performance compared to existing state-of-the-art brain tumor detection methods and pre-trained deep learning models.
  • Image enhancement using CLAHE effectively improved the visibility of tumor features for the PCNN classifier.

Conclusions:

  • The developed automated system offers a promising solution for accurate and efficient brain tumor detection from MRI scans.
  • The integration of CLAHE for image enhancement and PCNN for classification provides a robust approach to overcoming challenges in brain tumor diagnosis.
  • This research contributes to advancing automated medical image analysis for critical diseases like brain cancer.