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Aiming at the problems of low contrast, low detection accuracy and easy to miss detection and error detection of X-ray security image color in contraband detection task, based on Faster R-CNN algorithm, K-means clustering algorithm is used to improve the generation method of Anchor. It is proposed to introduce the efficient channel attention mechanism (ECANet) into the ROI pooling layer to highlight the contour, color and other information of contraband. The mAP of the proposed algorithm on the S_DXray dataset reaches 92.06%, and the detection accuracy of the improved network model is improved by 5.06 percentage points. It effectively improves the accuracy of X-ray image contraband detection and the detection ability of small-scale targets, and effectively avoids the phenomenon of false detection and missed detection. [ABSTRACT FROM AUTHOR]

A deep learning-based model for automatic diagnosis and classification of adolescent idiopathic scoliosis has been constructed. This model mainly included key points detection and Cobb angle measurement. 748 full-length standing spinal X-ray images were retrospectively collected, of which 602 images were used to train and validate the model, and 146 images were used to test the model performance. The results showed that the model had good diagnostic and classification performance, with an accuracy of 94.5%. Compared with experts' measurement, 94.9% of its Cobb angle measurement results were within the clinically acceptable range. The average absolute difference was 2.1°, and the consistency was also excellent (r2≥0.9552, P

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer-Aided Design & Computer Graphics / Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A deep learning-based model for automatic diagnosis and classification of adolescent idiopathic scoliosis has been constructed. This model mainly included key points detection and Cobb angle measurement. 748 full-length standing spinal X-ray images were retrospectively collected, of which 602 images were used to train and validate the model, and 146 images were used to test the model performance. The results showed that the model had good diagnostic and classification performance, with an accuracy of 94.5%. Compared with experts' measurement, 94.9% of its Cobb angle measurement results were within the clinically acceptable range. The average absolute difference was 2.1°, and the consistency was also excellent ( r 2 ≥0.9552, P <0.001). In the future, this model could be applied clinically to improve doctors' diagnostic efficiency.

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: To investigate the., Methods: for locating and selecting the acupoints of "Taixi" (KI3), "Shuiquan" (KI5), "Fuliu" (KI7), "Jiaoxin" (KI8), "Zhubin" (KI9), and "Yingu" (KI10) and the morphological structure of these acupoints in rabbits. MethodsAccording to the WHO and national standards for human acupoints and rabbit X-ray images, acupoint locations were marked using the anatomical landmarks on body surface in 10 New Zealand rabbits. The acupoints were dissected to compare the homologous and analogous tissue between rabbits and human body and thus correct the locations of these acupoints. Potentials were measured for the 10 New Zealand rabbits at the corrected locations of the acupoints and around the acupoints, and the final locations of these acupoints were determined by comparing the anatomical results and the data of potentials. Anatomical observation was performed after marking, and the relationship between acupuncture needle and adjacent structure was observed., Results: "Taixi" was located in the ankle area, at the midpoint between the prominence of the medial malleolus and the calca-neal tendon; "Shuiquan" was located in the calcaneal area below "Taixi" in the depression anterior to the calcaneal tuberosity; "Fuliu" was located at the medial side of the calf, at 2 cun above the prominence of the medial malleolus anterior to the calcaneal tendon; "Jiaoxin" was located at the medial side of the calf, at 2 cun above the prominence of the medial malleolus and in the depression posterior to the medial border of the tibia; "Zhubin" was located at the medial side of the calf, at 5 cun above the medial malleolus on the line between "Taixi" and "Yingu"; "Yingu" was located at the medial side of the knee, at the posterior-inferior border of the semitendinosus tendon on the popliteal crease. The results of skin potentials at the acupoints suggested that "Taixi", "Shuiquan", "Fuliu", and "Zhubin" were high-reliability acupoints, "Jiaoxin" was a medium-reliability acupoint, and "Yingu" was a low-reliability acupoint., Conclusion: Comparative anatomy combined with imaging, surface anatomy, and electrophysiological techniques of acupoints can help with the accurate localization and selection of acupoints in experimental animals, improve the reliability of acupoint location, and enrich the comparative anatomical data of acupoints.