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Intelligent Deep-Learning-Enabled Decision-Making Medical System for Pancreatic Tumor Classification on CT Images.

Thavavel Vaiyapuri1, Ashit Kumar Dutta2, I S Hephzi Punithavathi3

  • 1Department of Computer Sciences, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia.

Healthcare (Basel, Switzerland)
|April 23, 2022
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Summary

This study introduces an intelligent deep-learning system for pancreatic tumor classification using CT scans. The novel approach enhances early cancer detection and classification accuracy, improving patient outcomes.

Keywords:
artificial intelligencedecision-making systemsdeep learninghealthcare sectormachine learningmultilevel thresholding

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Oncology

Background:

  • Pancreatic cancer detection remains challenging with current methods.
  • Computer-aided diagnosis (CAD) models using radiological images offer potential for automated detection.
  • Machine learning (ML) and deep learning (DL) show promise for timely pancreatic cancer diagnosis.

Purpose of the Study:

  • To introduce an intelligent deep-learning-enabled decision-making medical system for pancreatic tumor classification (IDLDMS-PTC) using CT images.
  • To improve the accuracy and timeliness of pancreatic tumor detection and classification.
  • To leverage advanced optimization techniques for enhanced diagnostic performance.

Main Methods:

  • Utilized an emperor penguin optimizer with multilevel thresholding (EPO-MLT) for pancreatic tumor segmentation from CT images.
  • Employed the MobileNet model as a feature extractor.
  • Applied an optimal autoencoder (AE) for classification, with weights and biases tuned by the multileader optimization (MLO) technique.
  • Introduced novel EPO for threshold selection and MLO for parameter tuning.

Main Results:

  • The IDLDMS-PTC model demonstrated promising performance in simulations on benchmark datasets.
  • The system achieved high accuracy in classifying pancreatic tumors from CT images.
  • The combination of EPO-MLT, MobileNet, AE, and MLO proved effective for the task.

Conclusions:

  • The developed IDLDMS-PTC system offers a robust and intelligent solution for pancreatic tumor classification.
  • The proposed optimization techniques contribute to the novelty and effectiveness of the system.
  • This deep learning approach shows significant potential to enhance early detection and diagnosis of pancreatic cancer.