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Related Experiment Video

Updated: Nov 23, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

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Brain tumor detection and multi-classification using advanced deep learning techniques.

Tariq Sadad1, Amjad Rehman2, Asim Munir3

  • 1Department of Computer Science, University of Central Punjab, Lahore, Pakistan.

Microscopy Research and Technique
|January 5, 2021
PubMed
Summary

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This summary is machine-generated.

Automated brain tumor detection using deep learning models like NASNet achieved high accuracy. This advanced computer-assisted diagnosis improves early detection, crucial for effective treatment planning and patient survival rates.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Early brain tumor diagnosis is critical for treatment and survival.
  • Manual detection is complex, time-consuming, and error-prone.
  • Automated, high-precision computer-assisted diagnosis is in demand.

Purpose of the Study:

  • To develop and evaluate an automated system for brain tumor segmentation and classification.
  • To compare the performance of various deep learning models for brain tumor diagnosis.

Main Methods:

  • Brain tumor segmentation using U-Net architecture with ResNet50 backbone.
  • Image preprocessing and data augmentation to improve classification.
  • Multi-classification of brain tumors using evolutionary algorithms, reinforcement learning, and transfer learning.
Keywords:
NASNetWHObrain tumorcancerhealth riskshealthcare

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  • Application of deep learning models: ResNet50, DenseNet201, MobileNet V2, InceptionV3, and NASNet.

    • Main Results:

    • Achieved an Intersection over Union (IoU) of 0.9504 for segmentation.
    • NASNet model demonstrated the highest accuracy (99.6%) in tumor classification.
    • Proposed framework outperformed existing state-of-the-art methods.

    Conclusions:

    • The proposed automated system significantly enhances brain tumor detection accuracy.
    • Deep learning, particularly NASNet, shows great promise for precise and efficient brain tumor classification.
    • This approach can aid clinicians in early diagnosis and treatment planning.