Progress of Pathological Image Analysis Based on deep Learning

Pathological image analysis plays a crucial role in the diagnosis and treatment of diseases. However, due to its complexity and diversity, traditional methods are difficult to meet the growing demand. A novel method for analyzing pathological images has emerged in recent years because of advancements in deep learning technology, particularly the usage of convolutional neural networks (CNNs). This study compared the performance of various models through a systematic literature review and investigated the application status of deep learning in pathological image analysis, including pathological image classification, object detection and segmentation, and image generation and enhancement. According to studies, deep learning techniques greatly increase the precision and effectiveness of pathological picture analysis. This work not only emphasizes the significant potential of deep learning technology in enhancing the efficiency of pathological image processing but also brings out its practical application value in clinical practice. Furthermore, to further optimize the integration of deep learning into workflows for pathological image analysis, this study also analyzes the remaining obstacles and future objectives.