Advances in nano research

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Use of deep learning in nano image processing through the CNN model

  • Xing, Lumin (The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital) ;
  • Liu, Wenjian (City University of Macau) ;
  • Liu, Xiaoliang (The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital) ;
  • Li, Xin (Shandong University of Political Science and Law) ;
  • Wang, Han (Zhuhai Institute of Advanced Technology Chinese Academy of Sciences)
  • Received : 2021.11.20
  • Accepted : 2021.12.14
  • Published : 2022.02.25
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Abstract

Deep learning is another field of artificial intelligence (AI) utilized for computer aided diagnosis (CAD) and image processing in scientific research. Considering numerous mechanical repetitive tasks, reading image slices need time and improper with geographical limits, so the counting of image information is hard due to its strong subjectivity that raise the error ratio in misdiagnosis. Regarding the highest mortality rate of Lung cancer, there is a need for biopsy for determining its class for additional treatment. Deep learning has recently given strong tools in diagnose of lung cancer and making therapeutic regimen. However, identifying the pathological lung cancer's class by CT images in beginning phase because of the absence of powerful AI models and public training data set is difficult. Convolutional Neural Network (CNN) was proposed with its essential function in recognizing the pathological CT images. 472 patients subjected to staging FDG-PET/CT were selected in 2 months prior to surgery or biopsy. CNN was developed and showed the accuracy of 87%, 69%, and 69% in training, validation, and test sets, respectively, for T1-T2 and T3-T4 lung cancer classification. Subsequently, CNN (or deep learning) could improve the CT images' data set, indicating that the application of classifiers is adequate to accomplish better exactness in distinguishing pathological CT images that performs better than few deep learning models, such as ResNet-34, Alex Net, and Dense Net with or without Soft max weights.

Keywords

Acknowledgement

This work was supported in part by the Academic promotion program of Shandong First Medical University under Grant 2019LJ005.

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