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In recent years, Artificial Intelligence (AI) has gained significant attention in advancing automation across various domains. Terms such as Machine Learning (ML), Deep Learning (DL), Computer Vision (CV) and AI itself are frequently mentioned in various technical literature, research articles, and media. Nowadays, DL frameworks have emerged as the leading AI-based technology, dominating both academic research and industrial applications. They are widely employed for predictive tasks such as data analysis, text mining, image classification, image segmentation, object detection, human pose estimation, emotion detection, automatic vehicle driving, navigation and more. These AI related tasks are tremendously useful in various sectors in the modern era such as biomedicine, healthcare, Information and Communication Technology (ICT), digital technology and industrial automation. DL framework has a wide range of applications in medical imaging, proving invaluable support to healthcare professionals. These algorithms excel in a range of medical tasks, including disease classification, organ segmentation (anatomy modeling), lesion and abnormal tissue detection, and the generation of contrast-enhanced CT and MRI images.

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Deep Convolutional Neural Networks (CNNs)

  • 章节

  • 发表日期：

    2026年

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  • Yen-Wei Chen,
  • Lanfen Lin,
  • Rahul Kumar Jain

摘要

In recent years, Artificial Intelligence (AI) has gained significant attention in advancing automation across various domains. Terms such as Machine Learning (ML), Deep Learning (DL), Computer Vision (CV) and AI itself are frequently mentioned in various technical literature, research articles, and media. Nowadays, DL frameworks have emerged as the leading AI-based technology, dominating both academic research and industrial applications. They are widely employed for predictive tasks such as data analysis, text mining, image classification, image segmentation, object detection, human pose estimation, emotion detection, automatic vehicle driving, navigation and more. These AI related tasks are tremendously useful in various sectors in the modern era such as biomedicine, healthcare, Information and Communication Technology (ICT), digital technology and industrial automation. DL framework has a wide range of applications in medical imaging, proving invaluable support to healthcare professionals. These algorithms excel in a range of medical tasks, including disease classification, organ segmentation (anatomy modeling), lesion and abnormal tissue detection, and the generation of contrast-enhanced CT and MRI images.