Integrated Age Estimation Mechanism

Machine-learning-based age estimation has received lots of attention. Traditional age estimation mechanism focuses estimation age error, but ignores that there is a deviation between the estimated age and real age due to disease. Pathological age estimation mechanism the author proposed before introduces age deviation to solve the above problem and improves classification capability of the estimated age significantly. However,it does not consider the age estimation error of the normal control (NC) group and results in a larger error between the estimated age and real age of NC group. Therefore, an integrated age estimation mechanism based on Decision-Level fusion of error and deviation orientation model is proposed to solve the problem.Firstly, the traditional age estimation and pathological age estimation mechanisms are weighted together.Secondly, their optimal weights are obtained by minimizing mean absolute error (MAE) between the estimated age and real age of normal people. In the experimental section, several representative age-related datasets are used for verification of the proposed method. The results show that the proposed age estimation mechanism achieves a good tradeoff effect of age estimation. It not only improves the classification ability of the estimated age, but also reduces the age estimation error of the NC group. In general, the proposed age estimation mechanism is effective. Additionally, the mechanism is a framework mechanism that can be used to construct different specific age estimation algorithms, contributing to relevant research.

[1]  W. Tseng,et al.  Generalization of diffusion magnetic resonance imaging–based brain age prediction model through transfer learning , 2020, NeuroImage.

[2]  K. Maiese,et al.  Aging and Age-Related Disorders , 2010 .

[3]  Guodong Guo,et al.  Simultaneous dimensionality reduction and human age estimation via kernel partial least squares regression , 2011, CVPR 2011.

[4]  F. Dornaika,et al.  A comparative study of human facial age estimation: handcrafted features vs. deep features , 2020, Multimedia Tools and Applications.

[5]  San-Qiang Li,et al.  Measuring temporal expression, systematic response, and post-mortem stability to assess potential markers for estimating wound age: an example of Fosl1 in contused skeletal muscle , 2019 .

[6]  T. Beaini,et al.  Age estimation from dental cementum incremental lines and periodontal disease. , 2010, The Journal of forensic odonto-stomatology.

[7]  K. Jellinger.  Aging and Age-Related Disorders , 2011 .

[8]  Jianru Xue,et al.  Facial age estimation from web photos using multiple-instance learning , 2014, 2014 IEEE International Conference on Multimedia and Expo (ICME).

[9]  Hassan Ugail,et al.  Facial Age Synthesis Using Sparse Partial Least Squares (The Case of Ben Needham) , 2017, Journal of forensic sciences.

[10]  E. Salmon,et al.  Impairment of age estimation from faces in Alzheimer's disease. , 2015, Journal of Alzheimer's disease : JAD.

[11]  Cong Jin,et al.  Predicting healthy older adult's brain age based on structural connectivity networks using artificial neural networks , 2016, Comput. Methods Programs Biomed..

[12]  Stefan Klöppel,et al.  Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: Exploring the influence of various parameters , 2010, NeuroImage.

[13]  M. Qiu,et al.  Estimating the brain pathological age of Alzheimer’s disease patients from MR image data based on the separability distance criterion , 2016, Physics in medicine and biology.

[14]  Hala H. Zayed,et al.  Face Age Estimation Approach based on Deep Learning and Principle Component Analysis , 2018 .

[15]  W. Shim,et al.  Computerized Bone Age Estimation Using Deep Learning Based Program: Evaluation of the Accuracy and Efficiency. , 2017, AJR. American journal of roentgenology.

[16]  S. Fieuws,et al.  Dental age estimation in Somali children using the Willems et al. model , 2018, International Journal of Legal Medicine.

[17]  Haider Banka,et al.  New Hybrid PCA-Based Facial Age Estimation Using Inter-Age Group Variation-Based Hierarchical Classifier , 2017, Arabian Journal for Science and Engineering.

[18]  Vlad-Sebastian Ionescu,et al.  Machine learning techniques for age at death estimation from long bone lengths , 2016, 2016 IEEE 11th International Symposium on Applied Computational Intelligence and Informatics (SACI).

[19]  Michel F. Valstar,et al.  Small Sample Deep Learning for Newborn Gestational Age Estimation , 2017, 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017).

[20]  Lixin Duan,et al.  Hybrid constraint SVR for facial age estimation , 2014, Signal Process..

[21]  Christian Gaser,et al.  Longitudinal Changes in Individual BrainAGE in Healthy Aging, Mild Cognitive Impairment, and Alzheimer’s Disease , 2012 .

[22]  Xiang Li,et al.  Gait-based human age estimation using age group-dependent manifold learning and regression , 2018, Multimedia Tools and Applications.

[23]  Daniel S. Margulies,et al.  Predicting brain-age from multimodal imaging data captures cognitive impairment , 2016 .

[24]  Pin Wang,et al.  Dependency criterion based brain pathological age estimation of Alzheimer’s disease patients with MR scans , 2017, BioMedical Engineering OnLine.

[25]  Yongming Li,et al.  Improved Age Estimation Mechanism from Medical Data Based on Deep Instance Weighting Fusion , 2020 .

[26]  Theo Gevers,et al.  Expression-Invariant Age Estimation Using Structured Learning , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  Yi Fang,et al.  Serum Triglycerides Are Related to Chronic Kidney Disease (CKD) Stage 2 in Young and Middle-Aged Chinese Individuals During Routine Health Examination , 2019, Medical science monitor : international medical journal of experimental and clinical research.

[28]  M. Bots,et al.  Shifts in the age distribution and from acute to chronic coronary heart disease hospitalizations , 2016, European journal of preventive cardiology.

[29]  H. Matsuda,et al.  T1-weighted MRI-driven Brain Age Estimation in Alzheimer’s Disease and Parkinson’s Disease , 2019, Aging and Disease.

[30]  Chih-Yen Chen,et al.  Bone Age Pre-estimation Using Partial Least Squares Regression Analysis with a Priori Knowledge , 2014, 2014 IEEE International Symposium on Medical Measurements and Applications (MeMeA).

[31]  Yosi Keller,et al.  DeepAge: Deep Learning of face-based age estimation , 2019, Signal Process. Image Commun..

[32]  Naveen Kumar,et al.  Evaluation of cardiovascular disease in patients with systemic arterial hypertension in relation to age and sex: a retrospective study in a south Indian population , 2017, Jornal vascular brasileiro.

[33]  Christian Gaser,et al.  Advanced BrainAGE in older adults with type 2 diabetes mellitus , 2013, Front. Aging Neurosci..

[34]  Age-specific breast cancer risk by body mass index and familial risk: prospective family study cohort (ProF-SC) , 2018, Breast Cancer Research.

[35]  Wei‐Ju Lee,et al.  Large-Scale Structural Covariance Networks Predict Age in Middle-to-Late Adulthood: A Novel Brain Aging Biomarker. , 2020, Cerebral cortex.

[36]  Stefan Klöppel,et al.  BrainAGE in Mild Cognitive Impaired Patients: Predicting the Conversion to Alzheimer’s Disease , 2013, PloS one.