首页|Nanjing University of Aeronautics and Astronautics Reports Findings in Machine L earning (Study on breast cancerization and isolated diagnosis in situ by HOF-ATR -MIR spectroscopy with deep learning)
Nanjing University of Aeronautics and Astronautics Reports Findings in Machine L earning (Study on breast cancerization and isolated diagnosis in situ by HOF-ATR -MIR spectroscopy with deep learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting from Nanjing, People's Republ ic of China, by NewsRx journalists, research stated, "Mid-infrared (MIR) spectro scopy can characterize the content and structural changes of macromolecular comp onents in different breast tissues, which can be used for feature extraction and model training by machine learning to achieve accurate classification and recog nition of different breast tissues. In parallel, the one-dimensional convolution al neural network (1D-CNN) stands out in the field of deep learning for its abil ity to efficiently process sequential data, such as spectroscopic signals." The news correspondents obtained a quote from the research from the Nanjing Univ ersity of Aeronautics and Astronautics, "In this study, MIR spectra of breast ti ssue were collected in situ by coupling the self-developed MIR hollow optical fi ber attenuated total reflection (HOF-ATR) probe with a Fourier transform infrare d spectroscopy (FTIR) spectrometer. Staging analysis was conducted on the change s in macromolecular content and structure in breast cancer tissues. For the firs t time, a trinary classification model was established based on 1D-CNN for recog nizing normal, paracancerous and cancerous tissues. The final predication result s reveal that the 1D-CNN model based on baseline correction (BC) and data augmen tation yields more precise classification results, with a total accuracy of 95.0 9%, exhibiting superior discrimination ability than machine learnin g models of SVM-DA (90.00%), SVR (88.89%), PCA-FDA (67 .78%) and PCA-KNN (70.00%)."
NanjingPeople's Republic of ChinaAsiaCyborgsDiagnostics and ScreeningEmerging TechnologiesHealth and Medicin eMachine Learning
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