Recent Advancements and Future Prospects in Active Deep Learning for Medical Image Segmentation and Classification

Medical images are helpful for the diagnosis, treatment, and evaluation of diseases. Precise medical image segmentation improves diagnosis and decision-making, aiding intelligent medical services for better disease management and recovery. Due to the unique nature of medical images, image segmentation algorithms based on deep learning face problems such as sample imbalance, edge blur, false positives, and false negatives. In view of these problems, researchers primarily improve the network structure but rarely improve from the unstructured aspect. The paper tackles these challenges, accentuating the limitations of deep convolutional neural network-based methods and proposing solutions to reduce annotation costs, particularly in complex images, and introduces the improvement strategies to solve the problems of sample imbalance, edge blur, false positives, and false negatives. Additionally, the article introduces the latest deep learning-based applications in medical image analysis, covering segmentation, image acquisition, enhancement, registration, and classification. Moreover, the article provides an overview of four cutting-edge deep learning models, namely convolutional neural network (CNN), deep belief network (DBN), stacked autoencoder (SAE), and recurrent neural network (RNN). The study selection involved searching benchmark academic databases, collecting relevant literature and appropriate indicator for analysis, emphasizing DL-based segmentation and classification approaches, and evaluating performance metrics. The research highlights clinicians' and scholars' obstacles in developing an efficient and accurate malignancy prognostic framework based on state-of-the-art deep-learning algorithms. Furthermore, future perspectives are explored to overcome challenges and advance the field of medical image analysis.