Abstract
In Traditional Chinese Medicine (TCM), facial features are important basis for diagnosis and treatment. A doctor of TCM can prescribe according to a patient’s physical indicators such as face, tongue, voice, symptoms, pulse. Previous works analyze and generate prescription according to symptoms. However, research work to mine the association between facial features and prescriptions has not been found for the time being. In this work, we try to use deep learning methods to mine the relationship between the patient’s face and herbal prescriptions (TCM prescriptions), and propose to construct convolutional neural networks that generate TCM prescriptions according to the patient’s face image. It is a novel and challenging job. In order to mine features from different granularities of faces, we design a multi-scale convolutional neural network based on three-grained face, which mines the patient’s face information from the organs, local regions, and the entire face. Our experiments show that convolutional neural networks can learn relevant information from face to prescribe, and the multi-scale convolutional neural networks based on three-grained face perform better.
Similar content being viewed by others
References
Anthimopoulos M, Christodoulidis S, Ebner L, Christe A, Mougiakakou S (2016) Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans Med Imaging 35(5):1207–1216
Bayramoglu N, Kannala J, Heikkilä J (2016) Deep learning for magnification independent breast cancer histopathology image classification. In: 2016 23rd International conference on pattern recognition (ICPR), pp 2440–2445
Bottou L (2012) Stochastic gradient descent tricks. In: Neural networks: tricks of the trade. 9780201398298. Springer, Berlin, pp 421–436
Breiman L (2001) Random forests. Mach Learn 45(1):5–32
Chaabouni S, Benois-pineau J, Tison F, Ben Amar C, Zemmari A (2017) Prediction of visual attention with deep cnn on artificially degraded videos for studies of attention of patients with dementia. Multimed Tools Appl 76(21):22527–22546
Chen M, Shi X, Zhang Y, Wu D, Guizani M (2017) Deep features learning for medical image analysis with convolutional autoencoder neural network. IEEE Trans Big Data, 1
Cheung F (2011) TCM: made in China. Nature 480:S82
Chougrad H, Zouaki H, Alheyane O (2018) Deep convolutional neural networks for breast cancer screening. Comput Methods Programs Biomed 157:19–30
Dehan L, Jia W, Yimin C, Hamid G (2014) Classification of Chinese herbal medicines based on SVM. In: 2014 International conference on information science, electronics and electrical engineering, vol 1, pp 453–456
Glorot X, Bordes A, Bengio Y (2011) Deep sparse rectifier neural networks. In: AISTATS ’11: Proceedings of the 14th international conference on artificial intelligence and statistics, pp 315–323, 1502.03167
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 770–778
Hon M, Khan NM (2017) Towards Alzheimer’s disease classification through transfer learning. In: 2017 IEEE International conference on bioinformatics and biomedicine (BIBM), pp 1166–1169
Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: The IEEE Conference on computer vision and pattern recognition (CVPR)
Huang G, Liu Z, v d Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: 2017 IEEE Conference on computer vision and pattern recognition (CVPR), pp 2261–2269
Jain V, Learned-Miller E (2010) Fddb: a benchmark for face detection in unconstrained settings. Tech. Rep UM-CS-2010-009. University of Massachusetts, Amherst
Jones AL (2018) The influence of shape and colour cue classes on facial health perception. Evol Hum Behav 39(1):19–29
Kassim YM, Prasath VBS, Glinskii OV, Glinsky VV, Huxley VH, Palaniappan K (2017) Microvasculature segmentation of arterioles using deep CNN. In: 2017 IEEE International conference on image processing (ICIP), pp 580–584
King DE (2009) Dlib-ml: a machine learning toolkit. J Mach Learn Res 10:1755–1758
Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. Advances In Neural Information Processing Systems, pp 1097–1105, 1102.0183
Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, van der Laak JAWM, van Ginneken B, Sánchez C I (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88
Liu B, Zhou X, Wang Y, Hu J, He L, Zhang R, Chen S, Guo Y (2012) Data processing and analysis in real-world traditional Chinese medicine clinical data: challenges and approaches. Stat Med 31(7): 653–660
Peng H (1996) Dictionary of traditional Chinese medicine prescriptions. People Health Press, Beijing
Peng Y, Wang N, Wang Y, Wang M (2019) Segmentation of dermoscopy image using adversarial networks. Multimed Tools Appl 78(8):10965–10981
Qiu J (2007) Traditional medicine: a culture in the balance. Nature 448:126
Sekaran K, Chandana P, Krishna NM, Kadry S (2019) Deep learning convolutional neural network (cnn) with gaussian mixture model for predicting pancreatic cancer. Multimedia Tools and Applications
Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. Int Conf Learn Represent (ICRL) 1409:1556
Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958. 1102.4807
Stanitsas P, Cherian A, Truskinovsky A, Morellas V, Papanikolopoulos N (2017) Active convolutional neural networks for cancerous tissue recognition. In: 2017 IEEE International conference on image processing (ICIP), pp 1367–1371
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: 2015 IEEE Conference on computer vision and pattern recognition (CVPR), pp 1–9
Taigman Y, Yang M, Ranzato M, Wolf L (2014) DeepFace: closing the gap to human-level performance in face verification. In: 2014 IEEE Conference on computer vision and pattern recognition, pp 1701–1708
Wang J, Ding H, Bidgoli FA, Zhou B, Iribarren C, Molloi S, Baldi P (2017) Detecting cardiovascular disease from Mammograms with deep learning. IEEE Trans Med Imaging 36(5):1172–1181
Weng H, Liu Z, Yan S, Fan M, Ou A, Chen D, Hao T (2017) A framework for automated knowledge graph construction towards traditional Chinese medicine. In: Siuly S, Huang Z, Aickelin U, Zhou R, Wang H, Zhang Y, Klimenko S (eds) Health information science. Springer International Publishing, Cham, pp 170–181
Weng JC, Hu MC, Lan KC (2017) Recognition of easily-confused TCM herbs using deep learning. In: Proceedings of the 8th ACM on multimedia systems conference, MMSys’17. ACM, New York, pp 233–234
Xie D, Pei W, Zhu W, Li X (2017) Traditional Chinese medicine prescription mining based on abstract text. In: 2017 IEEE 19th International conference on e-health networking, applications and services (Healthcom), pp 1–5
Xie S, Girshick R, Dollár P, Tu Z, He K (2017) Aggregated residual transformations for deep neural networks. In: 2017 IEEE Conference on computer vision and pattern recognition (CVPR), pp 5987–5995
Xu Z, Liu X, Cheng XE, Song JL, Zhang JQ (2017) Diagnosis of cirrhosis stage via deep neural network. In: 2017 IEEE International conference on bioinformatics and biomedicine (BIBM), pp 745–749
Yao L, Zhang Y, Wei B, Wang W, Zhang Y, Ren X, Bian Y (2015) Discovering treatment pattern in traditional Chinese Medicine clinical cases by exploiting supervised topic model and domain knowledge. J Biomed Inform 58:260–267
Yao L, Zhang Y, Wei B, Zhang W, Jin Z (2018) A topic modeling approach for traditional Chinese medicine prescriptions. IEEE Trans Knowl Data Eng 30(6):1007–1021
Yiqin W (2012) Objective application of TCM inspection of face and tongue. Chin Arch Tradit Chin Med 30(2):349–352
Yu L, Chen H, Dou Q, Qin J, Heng PA (2017) Automated melanoma recognition in dermoscopy images via very deep residual networks. IEEE Trans Med Imaging 36(4):994–1004
Yu T, Li J, Yu Q, Tian Y, Shun X, Xu L, Zhu L, Gao H (2017) Knowledge graph for TCM health preservation: design, construction, and applications. Artif Intell Med 77:48–52
Yuan Y, Chao M, Lo YC (2017) Automatic skin lesion segmentation using deep fully convolutional networks with Jaccard distance. IEEE Trans Med Imaging 36 (9):1876–1886
Zagoruyko S, Komodakis N (2016) Wide residual networks. In: Wilson RCERH, Smith WAP (eds) Proceedings of the British machine vision conference (BMVC). BMVA Press, pp 87.1–87.12
Zhang ML, Zhou ZH (2014) A review on multi-label learning algorithms. IEEE Trans Knowl Data Eng 26(8):1819–1837
Zhang NL, Zhang R, Chen T (2012) Discovery of regularities in the use of herbs in traditional Chinese medicine prescriptions. In: Cao L, Huang JZ, Bailey J, Koh Y S, Luo J (eds) New frontiers in applied data mining. Springer, Berlin, pp 353–360
Zhao Y, Dong Q, Chen H, Iraji A, Li Y, Makkie M, Kou Z, Liu T (2017) Constructing fine-granularity functional brain network atlases via deep convolutional autoencoder. Med Image Anal 42:200–211
Zhao X, Wu Y, Song G, Li Z, Zhang Y, Fan Y (2018) A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. Med Image Anal 43:98–111
Zheng G, Jiang M, Lu C, Lu A (2014) Prescription analysis and mining. Springer International Publishing, Cham, pp 97–109
Zhu X, Liu Y, Li Q, Zhang Y, Wen C (2019) Mining patterns of chinese medicinal prescription for diabetes mellitus based on therapeutic effect. Multimedia Tools and Applications
Acknowledgements
This study was supported by the China National Science Foundation (60973083, 61273363), Science and Technology Planning Project of Guangdong Province (2014A010103009, 2015A020217002), and Guangzhou Science and Technology Planning Project (201504291154480, 2016040- 20179, 201803010088).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Liao, H., Wen, G., Hu, Y. et al. Convolutional herbal prescription building method from multi-scale facial features. Multimed Tools Appl 78, 35665–35688 (2019). https://doi.org/10.1007/s11042-019-08118-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-019-08118-7