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Related Concept Videos

Computed Tomography01:10

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Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
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Updated: Jun 1, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Deep learning models for CT image classification: a comprehensive literature review.

Isah Salim Ahmad1,2, Jingjing Dai1,2, Yaoqin Xie1,2

  • 1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.

Quantitative Imaging in Medicine and Surgery
|January 22, 2025
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Summary

Deep learning (DL) significantly enhances computed tomography (CT) image analysis for detecting diseases like COVID-19 and lung nodules. Advanced DL models improve diagnostic accuracy and efficiency, though challenges in implementation remain.

Keywords:
Computed tomography (CT)coronavirus disease 2019 (COVID-19)deep learning (DL)foundation modelsnodule detection

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Area of Science:

  • Medical Imaging and Diagnostics
  • Artificial Intelligence in Healthcare
  • Radiology and Oncology

Background:

  • Computed tomography (CT) is vital for diagnosing critical illnesses, especially respiratory diseases and cancers.
  • Deep learning (DL) is revolutionizing CT image analysis, improving diagnostic accuracy and efficiency.
  • This review focuses on DL applications in COVID-19 detection and lung nodule classification using CT.

Purpose of the Study:

  • To review the impact of advanced deep learning methodologies on CT imaging analysis.
  • To highlight DL applications in COVID-19 detection and lung nodule classification.
  • To explore the evolution of DL architectures in medical imaging.

Main Methods:

  • Comprehensive literature search of DL in CT image analysis from 2013-2023.
  • Examined evolution from convolutional neural networks (CNNs) to foundational models (FMs).
  • Focused on peer-reviewed research and review articles from major databases.

Main Results:

  • Deep learning, especially foundational models, has transformed CT image analysis, enhancing diagnostic capabilities.
  • Significant advancements were observed in COVID-19 detection and lung cancer screening.
  • Technical challenges include data variability, dataset size, and computational demands, with solutions like transfer learning and data augmentation.

Conclusions:

  • Deep learning plays a pivotal role in advancing CT analysis for COVID-19 and lung nodule detection.
  • Integration of DL models into clinical workflows promises enhanced diagnostic accuracy and efficiency.
  • Continued research, collaboration, and ethical considerations are crucial for DL's clinical integration.