| المؤلفون: |
Liu Y; School of Microelectronics, University of Science and Technology of China, Hefei, Anhui 230026, China., Yu S; School of Microelectronics, University of Science and Technology of China, Hefei, Anhui 230026, China., Zhang Z; School of Microelectronics, University of Science and Technology of China, Hefei, Anhui 230026, China., Hou X; School of Microelectronics, University of Science and Technology of China, Hefei, Anhui 230026, China., Ding M; School of Microelectronics, University of Science and Technology of China, Hefei, Anhui 230026, China., Zhao X; School of Microelectronics, University of Science and Technology of China, Hefei, Anhui 230026, China., Xu G; School of Microelectronics, University of Science and Technology of China, Hefei, Anhui 230026, China., Zhou X; School of Microelectronics, University of Science and Technology of China, Hefei, Anhui 230026, China., Long S; School of Microelectronics, University of Science and Technology of China, Hefei, Anhui 230026, China. |
| مستخلص: |
Recognition and judgment of X-ray computed tomography (CT) images play a crucial role in medical diagnosis and disease prevention. However, the storage and calculation of the X-ray imaging system applied in the traditional CT diagnosis is separate, and the pathological judgment is based on doctors' experience, which will affect the timeliness and accuracy of decision-making. In this paper, a simple-structured reservoir computing network (RC) is proposed based on Ga 2 O 3 X-ray optical synaptic devices to recognize medical skeletal CT images with high accuracy. Through oxygen vacancy engineering, Ga 2 O 3 X-ray optical synaptic devices with adjustable photocurrent gain and a persistent photoconductivity effect were obtained. By using the Ga 2 O 3 X-ray optical synaptic device as a reservoir, we constructed an RC network for medical skeletal CT diagnosis and verified its image recognition capability using the MNIST data set with an accuracy of 78.08%. In the elbow skeletal CT image recognition task, the recognition rate is as high as 100%. This work constructs a simple-structured RC network for X-ray image recognition, which is of great significance in applications in medical fields. |
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