A Review of Application of Deep Learning in Endoscopic Image Processing
Deep learning, particularly convolutional neural networks (CNNs), has revolutionized endoscopic image processing, significantly enhancing the efficiency and accuracy of disease diagnosis through its exceptional ability to extract features and classify complex patterns. This technology automates medi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/10/11/275 |
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| author | Zihan Nie Muhao Xu Zhiyong Wang Xiaoqi Lu Weiye Song |
| author_facet | Zihan Nie Muhao Xu Zhiyong Wang Xiaoqi Lu Weiye Song |
| author_sort | Zihan Nie |
| collection | DOAJ |
| description | Deep learning, particularly convolutional neural networks (CNNs), has revolutionized endoscopic image processing, significantly enhancing the efficiency and accuracy of disease diagnosis through its exceptional ability to extract features and classify complex patterns. This technology automates medical image analysis, alleviating the workload of physicians and enabling a more focused and personalized approach to patient care. However, despite these remarkable achievements, there are still opportunities to further optimize deep learning models for endoscopic image analysis, including addressing limitations such as the requirement for large annotated datasets and the challenge of achieving higher diagnostic precision, particularly for rare or subtle pathologies. This review comprehensively examines the profound impact of deep learning on endoscopic image processing, highlighting its current strengths and limitations. It also explores potential future directions for research and development, outlining strategies to overcome existing challenges and facilitate the integration of deep learning into clinical practice. Ultimately, the goal is to contribute to the ongoing advancement of medical imaging technologies, leading to more accurate, personalized, and optimized medical care for patients. |
| format | Article |
| id | doaj-art-0c35d7bf66564bdabf6681dfc35e7bf8 |
| institution | OA Journals |
| issn | 2313-433X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj-art-0c35d7bf66564bdabf6681dfc35e7bf82025-08-20T01:54:02ZengMDPI AGJournal of Imaging2313-433X2024-11-01101127510.3390/jimaging10110275A Review of Application of Deep Learning in Endoscopic Image ProcessingZihan Nie0Muhao Xu1Zhiyong Wang2Xiaoqi Lu3Weiye Song4School of Mechanical Engineering, Shandong University, Jinan 250061, ChinaSchool of Mechanical Engineering, Shandong University, Jinan 250061, ChinaSchool of Mechanical Engineering, Shandong University, Jinan 250061, ChinaSchool of Mechanical Engineering, Shandong University, Jinan 250061, ChinaSchool of Mechanical Engineering, Shandong University, Jinan 250061, ChinaDeep learning, particularly convolutional neural networks (CNNs), has revolutionized endoscopic image processing, significantly enhancing the efficiency and accuracy of disease diagnosis through its exceptional ability to extract features and classify complex patterns. This technology automates medical image analysis, alleviating the workload of physicians and enabling a more focused and personalized approach to patient care. However, despite these remarkable achievements, there are still opportunities to further optimize deep learning models for endoscopic image analysis, including addressing limitations such as the requirement for large annotated datasets and the challenge of achieving higher diagnostic precision, particularly for rare or subtle pathologies. This review comprehensively examines the profound impact of deep learning on endoscopic image processing, highlighting its current strengths and limitations. It also explores potential future directions for research and development, outlining strategies to overcome existing challenges and facilitate the integration of deep learning into clinical practice. Ultimately, the goal is to contribute to the ongoing advancement of medical imaging technologies, leading to more accurate, personalized, and optimized medical care for patients.https://www.mdpi.com/2313-433X/10/11/275deep learningendoscopyimage analysisconvolutional neural networks (CNNs) |
| spellingShingle | Zihan Nie Muhao Xu Zhiyong Wang Xiaoqi Lu Weiye Song A Review of Application of Deep Learning in Endoscopic Image Processing Journal of Imaging deep learning endoscopy image analysis convolutional neural networks (CNNs) |
| title | A Review of Application of Deep Learning in Endoscopic Image Processing |
| title_full | A Review of Application of Deep Learning in Endoscopic Image Processing |
| title_fullStr | A Review of Application of Deep Learning in Endoscopic Image Processing |
| title_full_unstemmed | A Review of Application of Deep Learning in Endoscopic Image Processing |
| title_short | A Review of Application of Deep Learning in Endoscopic Image Processing |
| title_sort | review of application of deep learning in endoscopic image processing |
| topic | deep learning endoscopy image analysis convolutional neural networks (CNNs) |
| url | https://www.mdpi.com/2313-433X/10/11/275 |
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