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Multidimensional CNN-Based Deep Segmentation Method for Tumor Identification.

R John Martin1, Uttam Sharma2, Kiranjeet Kaur3

  • 1Faculty of Computer Science and Information Technology, Jazan University, Saudi Arabia.

Biomed Research International
|September 1, 2022
PubMed
Summary
This summary is machine-generated.

A convolutional neural network (CNN) effectively segments nasopharyngeal cancer tumors using multimodal MR images. Multimodal fusion models significantly outperform single-modal approaches for precise tumor detection and segmentation.

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Area of Science:

  • Medical imaging analysis
  • Artificial intelligence in oncology
  • Radiology

Background:

  • Nasopharyngeal cancer (NPC) diagnosis relies on accurate tumor segmentation from medical images.
  • Current segmentation methods may face challenges with complex tumor characteristics in MR images.

Purpose of the Study:

  • To evaluate the performance of a convolutional neural network (CNN) for segmenting nasopharyngeal cancer tumors.
  • To compare the efficacy of single-modal, two-modal, and multimodal information fusion strategies within a CNN framework.

Main Methods:

  • Weighted MR images from 421 nasopharyngeal cancer patients were analyzed.
  • A dataset of 346 patients was used for training and 75 for independent testing.
  • Convolutional neural network models, including multimodal multidimensional information fusion (MMMDF), were employed for tumor segmentation.

Main Results:

  • The multimodal multidimensional information fusion model demonstrated the highest performance in tumor segmentation.
  • Two-modal fusion models performed second best, outperforming single-modal models.
  • The CNN accurately and efficiently segmented tumors in MR images of nasopharyngeal cancer.

Conclusions:

  • Multimodal data fusion significantly enhances the accuracy of CNN-based tumor segmentation in nasopharyngeal cancer.
  • The developed CNN models offer a precise and efficient tool for nasopharyngeal cancer diagnosis and treatment planning.