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Novel neural network model for predicting susceptibility of facial post-inflammatory hyperpigmentation.
- Source :
-
Medical Engineering & Physics . Dec2022, Vol. 110, pN.PAG-N.PAG. 1p. - Publication Year :
- 2022
-
Abstract
- • Novel deep learning architecture for PIH susceptibility prediction. • First introduction of Multi-head self-attention into prediction of PIH susceptibility. • Novel architecture of input data. • Incorporation of all indicators reflecting comprehensive skin conditions. : To construct a neural network model (ATBP) for predicting susceptibility to Post-inflammatory hyperpigmentation (PIH), which is a rapid, objective, and reliable decision-support method before physical and chemical interventions in dermatology clinics for pigment disorders. : A dataset was established based on the VISIA Skin Analysis System detection results of 1953 patients with pigment disorders including 93,477 labeled data under 8 indicators. A novel Post-inflammatory hyperpigmentation susceptibility prediction model incorporating Multi-head self-attention mechanism and Back-propagation neural network is proposed to capture the patterns of skin detection data to predict PIH susceptibility. : The results of comparison experiments indicate that Attentive BP (Back Propagation Neural Network) has a significant superiority in prediction accuracy (0.8604) compared with other machine learning models. The ablation experiments prove that the Multi-head self-attention mechanism substantially improves the accuracy and the stability of prediction. The results of the 10-fold cross-validation experiment prove that ATBP is robust and avoids turbulence in predicting. : Leveraging Multi-head self-attention mechanism and the architecture advantage of BPNN, the proposed model ATBP obtains the robust and efficient prediction performance in predicting PIH susceptibility via processing large-scale and hi-dimension data, i.e., considering comprehensive skin conditions of individual patient. It can be proved from the experimental results that the proposed model is reliable for decision-support work of PIH susceptibility. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 13504533
- Volume :
- 110
- Database :
- Academic Search Index
- Journal :
- Medical Engineering & Physics
- Publication Type :
- Academic Journal
- Accession number :
- 160910359
- Full Text :
- https://doi.org/10.1016/j.medengphy.2022.103884