A DNA-Level Convolutional Neural Network Based on Strand Displacement Reaction for Image Recognition
Over the past few decades, convolutional neural network (CNN) has found broad applications in image recognition. Nevertheless, the operational environment of CNN is facing significant challenges, including poor parallelism and high energy consumption due to the explosive growth in data scale and the...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11119641/ |
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| author | Shiyin Li Zhixiang Yin Chunlin Chen Jing Yang Zhen Tang |
| author_facet | Shiyin Li Zhixiang Yin Chunlin Chen Jing Yang Zhen Tang |
| author_sort | Shiyin Li |
| collection | DOAJ |
| description | Over the past few decades, convolutional neural network (CNN) has found broad applications in image recognition. Nevertheless, the operational environment of CNN is facing significant challenges, including poor parallelism and high energy consumption due to the explosive growth in data scale and the rise of multi-modal data. DNA-based molecular computing systems, which are known for their high parallelism and low energy consumption, have the potential to provide an ideal operating platform for CNN. Here, we employed strand displacement reaction (SDR) to construct a DNA-level CNN. Firstly, we integrated DNA-based weighted-sum module, subtraction activation module, and reporter module using SDR to implement CNN, and designed the weighted shared boxes to perform the function of convolutional kernel. The feasibility and parallelism of the proposed DNA-level CNN were verified by simultaneous recognition of three different categories of images, including handwritten numbers, letters and Chinese characters. We further applied the DNA-level CNN to parallel recognition of multi-class medical images. Visual DSD software was employed to perform all simulation experiments. The efficient parallel computing characteristics of DNA-level CNN provides a new approach for image recognition, and show great potential in multi-class medical image recognition. |
| format | Article |
| id | doaj-art-bbc4d8d03ed44f25b031c6ee97c23f6a |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-bbc4d8d03ed44f25b031c6ee97c23f6a2025-08-20T04:01:00ZengIEEEIEEE Access2169-35362025-01-011313970413971810.1109/ACCESS.2025.359678611119641A DNA-Level Convolutional Neural Network Based on Strand Displacement Reaction for Image RecognitionShiyin Li0Zhixiang Yin1Chunlin Chen2Jing Yang3Zhen Tang4https://orcid.org/0009-0007-9702-4947School of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai, ChinaSchool of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai, ChinaSchool of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai, ChinaSchool of Mathematics and Big Data, Anhui University of Science and Technology, Huainan, Anhui, ChinaSchool of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai, ChinaOver the past few decades, convolutional neural network (CNN) has found broad applications in image recognition. Nevertheless, the operational environment of CNN is facing significant challenges, including poor parallelism and high energy consumption due to the explosive growth in data scale and the rise of multi-modal data. DNA-based molecular computing systems, which are known for their high parallelism and low energy consumption, have the potential to provide an ideal operating platform for CNN. Here, we employed strand displacement reaction (SDR) to construct a DNA-level CNN. Firstly, we integrated DNA-based weighted-sum module, subtraction activation module, and reporter module using SDR to implement CNN, and designed the weighted shared boxes to perform the function of convolutional kernel. The feasibility and parallelism of the proposed DNA-level CNN were verified by simultaneous recognition of three different categories of images, including handwritten numbers, letters and Chinese characters. We further applied the DNA-level CNN to parallel recognition of multi-class medical images. Visual DSD software was employed to perform all simulation experiments. The efficient parallel computing characteristics of DNA-level CNN provides a new approach for image recognition, and show great potential in multi-class medical image recognition.https://ieeexplore.ieee.org/document/11119641/Convolutional neural networkDNA computingimage recognitionstrand displacement reaction |
| spellingShingle | Shiyin Li Zhixiang Yin Chunlin Chen Jing Yang Zhen Tang A DNA-Level Convolutional Neural Network Based on Strand Displacement Reaction for Image Recognition IEEE Access Convolutional neural network DNA computing image recognition strand displacement reaction |
| title | A DNA-Level Convolutional Neural Network Based on Strand Displacement Reaction for Image Recognition |
| title_full | A DNA-Level Convolutional Neural Network Based on Strand Displacement Reaction for Image Recognition |
| title_fullStr | A DNA-Level Convolutional Neural Network Based on Strand Displacement Reaction for Image Recognition |
| title_full_unstemmed | A DNA-Level Convolutional Neural Network Based on Strand Displacement Reaction for Image Recognition |
| title_short | A DNA-Level Convolutional Neural Network Based on Strand Displacement Reaction for Image Recognition |
| title_sort | dna level convolutional neural network based on strand displacement reaction for image recognition |
| topic | Convolutional neural network DNA computing image recognition strand displacement reaction |
| url | https://ieeexplore.ieee.org/document/11119641/ |
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