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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Automatic smart brain tumor classification and prediction system using deep learning.

Qurat Ul Ain Ishfaq1, Rozi Bibi1, Abid Ali2,3

  • 1Department of Computer Science, GPGC(W), Haripur, Pakistan.

Scientific Reports
|April 28, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a smart monitoring system using deep learning for early brain tumor detection and classification. The system achieved high accuracy, with models reaching up to 99.76% in classifying brain tumors from MRI scans.

Keywords:
Brain tumorCNNDeep learningEfficient-b4Inception-v4Smart healthcare

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Area of Science:

  • Medical imaging analysis
  • Artificial intelligence in healthcare
  • Neurology

Background:

  • Brain tumors are a serious health concern with symptoms overlapping other neurological conditions, often delaying diagnosis.
  • Timely diagnosis is critical for effective treatment, improved patient outcomes, and selecting appropriate therapies.
  • Early detection of brain tumors can prevent advanced stages, reduce complications, and enhance recovery rates.

Purpose of the Study:

  • To propose a smart monitoring system for early and timely detection, classification, and prediction of brain tumors.
  • To develop and evaluate deep learning models for brain tumor classification using MRI datasets.
  • To assess the performance of custom CNN, Inception-v4, and EfficientNet-B4 models in identifying ten brain tumor categories.

Main Methods:

  • A custom Convolutional Neural Network (CNN) model was developed for computational efficiency and adaptability in brain tumor classification.
  • Two pre-trained models, Inception-v4 and EfficientNet-B4, were employed alongside the custom CNN for classifying brain tumor cases.
  • The models were trained and evaluated on diverse brain MRI datasets, using metrics such as accuracy, precision, sensitivity, and specificity.

Main Results:

  • The custom CNN model achieved an average classification accuracy of 97.58%.
  • Pre-trained models demonstrated superior performance, with Inception-v4 reaching 99.56% and EfficientNet-B4 achieving 99.76% average accuracy.
  • On a test dataset of 1000 images, the models predicted accuracies of 96.5% (CNN), 99.3% (Inception-v4), and 99.7% (EfficientNet-B4), indicating sustained performance post-deployment.

Conclusions:

  • The proposed smart monitoring system effectively utilizes deep learning for accurate brain tumor detection and classification.
  • Inception-v4 and EfficientNet-B4 models show excellent potential for real-world application in brain tumor diagnosis.
  • The study highlights the significance of advanced AI techniques in improving early detection and patient management for brain tumors.