This study examines the innovative applications and future prospects of Convolutional Neural Networks (CNN) in the field of medical image analysis. CNNs significantly enhance the accuracy and efficiency of medical image diagnostics through their powerful data processing and feature extraction capabilities. This review analyzes various CNN architectures and recent technological advancements, highlighting the importance of transfer learning and data augmentation techniques. It also discusses the potential for integrated multi-modality data analysis and real-time clinical applications, while emphasizing the need for ethical considerations and data security. This research underscores the potential of CNN technology to improve healthcare quality and contribute to patient health management.

Abstract
1. 서 론
2. 기술적 배경
3. 문헌 리 뷰
4. 적용 분야
    4.1 질병 진단 및 예측
    4.2. 영상 분할 및 병변 탐지
    4.3. 정량적 분석과 치료 효과의 평가
    4.4. 의료 영상 통합 및 복합 분석
5. 모 델링 의 개발 절차
6. 논 의
References

• 변해원(Dept. of Future Technology, Korea University of Technology and Education, South Korea) | Haewon Byeon Corresponding author