A Review of Application of Deep Learning in Endoscopic Image Processing

• Published in: Journal of imaging 2024-11, Vol.10 (11), p.275
• Main Authors: Nie, Zihan, Xu, Muhao, Wang, Zhiyong, Lu, Xiaoqi, Song, Weiye
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial intelligence; Artificial neural networks; Automation; Clinical outcomes; Comparative analysis; convolutional neural networks (CNNs); Customization; Deep learning; Disease prevention; Efficiency; Endoscopy; Health services; Image analysis; Image enhancement; Image processing; Impact analysis; Machine learning; Medical diagnosis; Medical imaging; Methods; Neural networks; R&D; Research & development; Review; Technology; Technology application; Technology assessment; Tomography; Ultrasonic imaging; Workloads
• ISSN: 2313-433X; 2313-433X
• DOI: 10.3390/jimaging10110275

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.