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An Efficient Multi-Scale Convolutional Neural Network Based Multi-Class Brain MRI Classification for SaMD.

Syed Ali Yazdan1, Rashid Ahmad1,2, Naeem Iqbal3

  • 1Department of Computer Science, Attock Campus, COMSATS University Islamabad, Attock 43600, Pakistan.

Tomography (Ann Arbor, Mich.)
|July 27, 2022
PubMed
Summary
This summary is machine-generated.

A novel Multi-Scale Convolutional Neural Network (MSCNN) effectively diagnoses brain tumors from MRI scans, outperforming existing models in accuracy and efficiency while reducing computational cost. This approach minimizes Rician noise impact for improved clinical application.

Keywords:
FSNLMMRIbrain tumorclassificationdeep learningmulti-scale convolutional neural network

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Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Healthcare
  • Neuro-oncology Diagnostics

Background:

  • Brain tumors necessitate precise diagnosis due to high mortality rates, with Magnetic Resonance Imaging (MRI) being a key tool.
  • Rician noise in MRI degrades image quality and complicates diagnosis, impacting treatment efficacy.
  • Existing deep learning models for brain tumor classification, like AlexNet and ResNet, face challenges with noise, varying tumor scales, and high computational costs.

Purpose of the Study:

  • To develop a robust Multi-Scale CNN (MSCNN) architecture for accurate brain tumor classification (glioma, meningioma, pituitary, non-tumor).
  • To minimize the impact of Rician noise on CNN performance using image denoising techniques.
  • To enhance the accuracy and efficiency of brain tumor detection systems compared to state-of-the-art methods.

Main Methods:

  • Proposed a Multi-Scale CNN (MSCNN) architecture for multi-class brain tumor classification from MRI scans.
  • Applied a Fuzzy Similarity-based Non-Local Means (FSNLM) filter to denoise MRIs, mitigating Rician noise effects.
  • Evaluated model performance using accuracy, precision, recall, specificity, and F1-score, comparing MSCNN against AlexNet and ResNet.

Main Results:

  • The proposed MSCNN model achieved superior accuracy (91.2%) and F1-score (91%) compared to AlexNet and ResNet.
  • The MSCNN demonstrated higher efficiency and lower computational cost due to fewer trainable parameters.
  • Denoising MRIs with FSNLM filter further improved classification results, highlighting the importance of noise reduction.

Conclusions:

  • The developed MSCNN model offers a more accurate and efficient solution for brain tumor classification from MRI.
  • The study confirms the effectiveness of the proposed MSCNN in handling varying tumor scales and Rician noise.
  • The findings support the proposed model's potential to enhance clinical research and practice in MRI-based tumor diagnosis.