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Learning Architecture for Brain Tumor Classification Based on Deep Convolutional Neural Network: Classic and

Rabei Raad Ali1, Noorayisahbe Mohd Yaacob2, Marwan Harb Alqaryouti3

  • 1Technical Engineering College for Computer and AI, Northern Technical University, Mosul 41000, Iraq.

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|March 13, 2025
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Summary
This summary is machine-generated.

Convolutional Neural Networks (CNNs) accurately classify brain tumors in MRI scans. The ResNet50 architecture achieved 99.88% accuracy, significantly aiding diagnosis and treatment planning.

Keywords:
Convolutional Neural NetworksResNet-50deep learningimage classificationmagnetic resonance imaging

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Accurate brain tumor classification from medical images is crucial for patient survival.
  • Convolutional Neural Networks (CNNs) are investigated for enhancing diagnostic accuracy.
  • Magnetic Resonance Imaging (MRI) datasets are utilized for this classification task.

Purpose of the Study:

  • To evaluate the effectiveness of CNNs, specifically Classic layer and ResNet50 architectures, for brain tumor classification.
  • To compare the performance of different CNN models on a brain tumor MRI dataset.
  • To demonstrate the potential of advanced CNN architectures in improving diagnostic accuracy.

Main Methods:

  • A dataset of brain tumor MRI scans (200 × 200 × 1 resolution) was pre-processed.
  • Tumors were categorized into Glioma, Meningioma, and Pituitary types.
  • Classic layer and ResNet50 CNN architectures were trained and tested using an 80:20 split.

Main Results:

  • Both CNN architectures demonstrated high accuracy in classifying brain tumors.
  • The Classic layer architecture achieved 94.55% accuracy.
  • The ResNet50 architecture achieved a superior accuracy of 99.88%, outperforming previous studies.

Conclusions:

  • CNNs, particularly the ResNet50 architecture, are highly effective for brain tumor classification using MRI data.
  • These findings highlight the potential of CNNs to assist medical professionals in accurate diagnosis and treatment planning.
  • Future research should explore transfer learning to enhance performance, especially with limited annotated data.