Infantile hemangiomas (IH) and venous malformations (VM) are two common vascular diseases that occur during childhood. Both diseases present similar features in grayscale ultrasound images; therefore, using separate grayscale ultrasound images to classify IH and VM can result in misclassification owing to insufficient comprehensible information in the single-modality images. To address this problem, this study proposes an ultrasound multimodal classification model. The model uses grayscale ultrasound images and the corresponding color ultrasound images of IH and VM as input to the feature extraction modules. The feature outputs from the two modules are spliced and fused. Finally, the fused features are input into the fully connected layer to be classified and recognized. The data consists of 1498 grayscale images and 1498 color images. The experimental results showed that the accuracy of the ultrasound multimodal classification model was 96.9%, which was higher than that of seven existing unimodal models.
In Chinese acupuncture, moxibustion of the abdomen can energize the meridians, dissipate cold and relieve pain, promote water retention and reduce swelling, and warm the kidney and yang. The traditional abdominal acupoint identification, which mainly relies on the experience of doctors, is highly subjective and uncertain. To crack this problem, this paper proposes an abdominal meridian detection and abdominal acupoint location method based on geometric topological relations. First, we use MediaPipe to extract 33 human skeletal key points and YOLOv8 to detect the left and right nipples and navel, which are used as the reference points for detecting meridians and acupoints. Then the geometric topological relationships of the meridians and acupoints are established through the human body reference points, so as to determine the major meridians in the abdomen, such as the Conception Vessel Meridian, the Spleen Meridian, the Stomach Meridian, and the Sidney Meridian. Finally, under the guidance of the meridians, each acupoint in the abdomen was determined by the bone degree and cun method. The experimental results have showed that the method was able to accurately identify and localize the major 35 abdominal acupoints with an average error of less than 3 mm.
The artificial intelligence technology has been widely applied in agricultural production. In particular, scientists have designed special robots in different agricultural scenarios, such as Intelligent Pesticide Spraying Drone, Farm Weeding Robot, etc. Here we developed a fruit picking robot, where the accurate identification and discernment of fruits hold a significant role. The deep learning technique is applied to conduct fruit detection and recognition. This technique adopts YOLOv5s as the base network. The network has been trained with a collection of six types of fruit pictures. The empirical findings have unveiled the effectiveness of this fruit detection and recognition technique on diverse test image datasets from different kinds of fruits.
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