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Related Concept Videos

Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...

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Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging
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A New Model for Brain Tumor Detection Using Ensemble Transfer Learning and Quantum Variational Classifier.

Javeria Amin1, Muhammad Almas Anjum2, Muhammad Sharif3

  • 1Department of Computer Science, University of Wah, Wah 47040, Pakistan.

Computational Intelligence and Neuroscience
|April 25, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel AI model for accurate brain tumor detection and segmentation. The deep learning approach effectively classifies tumor types and analyzes severity, improving diagnostic accuracy.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • Early brain tumor detection is crucial for patient survival and effective treatment.
  • Manual tumor diagnosis is labor-intensive, prone to errors, and time-consuming.
  • There is a significant need for automated, accurate brain tumor diagnostic systems.

Purpose of the Study:

  • To develop and evaluate a deep learning model for accurate brain tumor classification and segmentation.
  • To utilize deep features from the inceptionv3 model for tumor discrimination.
  • To assess tumor severity through segmentation of infected regions.

Main Methods:

  • Deep features were extracted using the inceptionv3 model.
  • A quantum variational classifier (QVR) was employed for classifying glioma, meningioma, no tumor, and pituitary tumor.
  • A Seg-network was utilized for segmenting tumor regions to analyze severity.
  • The model was validated on Kaggle, 2020-BRATS, and local datasets.

Main Results:

  • The proposed model achieved high detection accuracy, exceeding 90% on benchmark datasets.
  • The system demonstrated effectiveness in discriminating between different tumor types and healthy tissue.
  • Segmentation analysis provided insights into tumor severity levels.

Conclusions:

  • The developed AI model shows significant promise for accurate and efficient brain tumor diagnosis.
  • The integration of deep learning and quantum classification offers a powerful approach for medical image analysis.
  • This research contributes to advancing automated diagnostic tools for neuro-oncology.