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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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ViT-PSO-SVM: Cervical Cancer Predication Based on Integrating Vision Transformer with Particle Swarm Optimization and

Abdulaziz AlMohimeed1, Mohamed Shehata2, Nora El-Rashidy3

  • 1College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia.

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|July 27, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel AI model, Vision Transformer with Particle Swarm Optimization and Support Vector Machine (ViT-PSO-SVM), for accurate, non-invasive cervical cancer detection. The AI approach significantly improves early diagnosis, potentially enhancing patient outcomes globally.

Keywords:
ViT-PSO-SVMcervical cancerdiagnostic model

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Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Cervical cancer (CCa) is a leading cause of cancer death in women globally, necessitating improved early detection methods.
  • Current diagnostic standards, like biopsy, are invasive; non-invasive imaging with high accuracy is highly desirable.
  • Artificial intelligence (AI), particularly Vision Transformers (ViT), shows potential in medical image analysis, rivaling traditional methods.

Purpose of the Study:

  • To evaluate the efficacy of a Vision Transformer (ViT) for predicting cervical cancer from cell images.
  • To develop and assess a novel hybrid AI model, ViT-PSO-SVM, for enhanced cervical cancer diagnosis.
  • To demonstrate the potential of AI as a reliable, non-invasive tool for improving cervical cancer detection and patient outcomes.

Main Methods:

  • Feature extraction using a Vision Transformer (ViT) from cervical cell image datasets (SipakMed and Herlev).
  • Optimization of extracted features using Particle Swarm Optimization (PSO) to reduce complexity and improve representation.
  • Classification of cervical cancer using a Support Vector Machine (SVM) model integrated with the ViT-PSO framework.
  • Evaluation of model performance across two, three, and five-class classification scenarios using accuracy and F1-score metrics.
  • Application of GradCAM for explainable AI (XAI) to visualize image regions critical for predictions.

Main Results:

  • The proposed ViT-PSO-SVM approach achieved high accuracy (99.112%) and F1-score (99.113%) on the SipakMed dataset (two-class).
  • The model demonstrated strong performance on the Herlev dataset, achieving 97.778% accuracy and 97.805% F1-score (two-class).
  • The ViT-PSO-SVM model outperformed existing ViT, CNN, and pre-trained models in cervical cancer classification tasks.
  • GradCAM visualizations provided insights into the model's decision-making process, confirming its focus on relevant image features.

Conclusions:

  • The developed ViT-PSO-SVM approach is a feasible and effective AI tool for accurate cervical cancer detection.
  • This AI-driven method offers a promising non-invasive alternative to traditional diagnostic procedures.
  • The model's high performance and explainability suggest its potential to significantly improve global healthcare outcomes for cervical cancer patients.