Wounds not only harm the physical and mental health of patients, but also introduce huge medical costs. Meanwhile, there is a shortage of physicians in some areas, and clinical examinations are sometimes unreliable in wound diagnosis. Reliable wound analysis is of great importance in its diagnosis, treatment, and care. Currently, deep learning has developed rapidly in the field of computer vision and medical imaging and has become the most commonly used technique in wound image analysis. This paper studies the current research on deep learning in the field of wound image analysis, including classification, detection, and segmentation. We first review the publicly available datasets from various research, and study the preprocessing methods used in wound image analysis. Second, various models used in different deep learning tasks (classification, detection, and segmentation) and their applications in different types of wounds (e.g., burns, diabetic foot ulcers, pressure ulcers) are investigated. Finally, we discuss the challenges in the field of wound image analysis using deep learning, and provide an outlook on the research and development prospects.

Brachial plexus block is a common regional anesthesia method widely used in upper limb surgery. Nowadays, ultrasound-guided brachial plexus block has been extensively used in clinical anesthesia. However, accurate brachial plexus block is highly dependent on the physician's experience, and a physician without extensive clinical experience may cause nerve injury when performing a nerve block. With the development of artificial intelligence technology, the deep learning method can automatically identify the brachial plexus in ultrasound images and assist doctors in completing the brachial plexus block accurately and quickly. In this paper, we aim to evaluate the performance of different deep learning models in identifying brachial plexus (i.e., segmentation of brachial plexus) from ultrasonic images to explore the best models and training strategies for this task. To this end, we use a new dataset containing 340 brachial plexus ultrasound images annotated by three experienced clinicians. Among the 12 deep learning models we evaluated, U-Net achieves the best segmentation accuracy, with an intersection over union (IoU) of 68.50%. However, the number of U-Net parameters is very large, and it can only process 15 images per second. Compared to U-Net, LinkNet can process 142 images per second and achieve the second-best segmentation accuracy with an IoU of 66.27%. It achieves the balance between segmentation accuracy and processing efficiency, which has a good potential for the brachial plexus's real-time segmentation task.

Deep learning has the advantages of high efficiency, high speed, high accuracy, and strong objectivity, and is widely used in the fields of pathology and laboratory diagnosis. The diagnostic techniques of traditional Chinese medicine are world-famous, and the four basic methods for diagnosing diseases, namely inspection, auscultation- olfaction, inquiry, and palpation, are collectively referred to as ”four diagnostics”. Tongue diagnosis is an important part of inspection, and it is also an effective diagnosis and treatment method for doctors to understand the changes of the patient’s body through the tongue image. In order to realize automatic tongue diagnosis, one of the important tasks is to implement the automatic segmentation of tongue images. However, using feature engineering to segment tongue images requires a lot of work, and only hand-crafted features cannot represent the features of the tongue well. Therefore, this paper designs a tongue segmentation network (TSN). TSN consists of three parts: feature encoding extraction module, context-aware module and feature decoding module. This model can fully extract tongue feature vector and perform information fusion through context-aware module, so that Effectively segment the tongue from the image. Compared with various deep learning image segmentation methods, the TSN proposed in this paper achieves the best performance results with 97.20% mean intersection over union (mIoU) and 98.83% pixel accuracy (PA).