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Related Experiment Video

Updated: Jun 23, 2026

Functional Magnetic Resonance Imaging (fMRI) of the Visual Cortex with Wide-View Retinotopic Stimulation
07:11

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Published on: December 8, 2023

Maximizing pancreatic carcinoma classification performance using parrot optimized vision transformer.

C Mallika1, E Dinesh2, Hadeel Alsolai3

  • 1Department of Master of Computer Applications, E.G.S. Pillay Engineering College, Nagapattinam, Tamil Nadu, 611002, India. cmallikachinna@gmail.com.

Scientific Reports
|May 21, 2026
PubMed
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This summary is machine-generated.

An AI model accurately identifies pancreatic cancer from CT scans, achieving 99% accuracy. This automated system offers a significant advancement for early detection of this rare disease.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Pancreatic cancer is often diagnosed late due to non-specific symptoms.
  • Early detection is crucial for improving patient outcomes.
  • Automated systems are needed for timely identification and classification.

Purpose of the Study:

  • To develop an AI model for classifying pancreatic cancer using CT images.
  • To enhance diagnostic accuracy and efficiency in pancreatic cancer detection.

Main Methods:

  • Utilized a Pancreatic CT image dataset (1411 images).
  • Applied image augmentation and Gabor filter preprocessing.
  • Employed UNet for segmentation, YOLOv11 for feature extraction.
  • Used Vision Transformer for classification, optimized with Parrot metaheuristic algorithm.
Keywords:
Gabor filterUNetVision transformer and parrot optimizerYOLOv11

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Last Updated: Jun 23, 2026

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Main Results:

  • Achieved 99% accuracy, 98.5% precision, 97.7% recall, and 96.4% F1-Score.
  • Demonstrated superior performance compared to Random Forest, CNN, DBN, and SVM models.
  • Reported a Matthew's correlation coefficient of 97.3%.

Conclusions:

  • The proposed AI model demonstrates high efficacy in classifying pancreatic cancer from CT images.
  • This approach offers a promising tool for early and accurate diagnosis.
  • The advanced deep learning techniques significantly outperform traditional machine learning methods.