A Review of Deep Learning Applications in Lesion Detection Research

As a powerful machine learning technique, deep learning has been widely applied to lesion detection in medical image processing. This review summarizes the research progress of deep learning applications in lesion detection. Firstly, the characteristics of medical image data are introduced, and the datasets and evaluation metrics of lesion detection are summarized. Then, the main contents of deep learning, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Generative Adversarial Networks (GAN), YOLO algorithm, and SAM, have demonstrated good performance in medical image processing. Meanwhile, the applications of lesion detection in different medical image modalities are discussed, and the advantages of deep learning in different lesion types are highlighted, such as high automation, good performance, and transferability. In addition, some challenges of deep learning in lesion detection are discussed, such as sample scarcity, interpretability, and reliability. Finally, the future development directions of deep learning in lesion detection are discussed, such as multimodal fusion, transfer learning, and labeled data. This review provides a comprehensive overview of the research progress of deep learning in the field of lesion detection, which offers guidance and reference for related research and applications.