scholarly journals Age group classification based on facial images

2014 ◽  
Author(s):  
Phyo Kyaw Sai
Keyword(s):  
Group Classification ◽  
Age Group ◽  
Age Group Classification ◽  
Facial Images

scholarly journals Age Group Classification of Facial Images Using Rank Based Edge Texture Unit (RETU) and Fuzzy Texture

2019 ◽  
pp. 605-611
Author(s):  
Jammula Nimitha ◽  
Kuraganti Nukeswari ◽  
Kuraganti Sudha ◽  
Kurapati Sumithra ◽  
Rama Devi Gunnam
Keyword(s):  
Age Groups ◽  
Group Classification ◽  
Facial Features ◽  
Age Group ◽  
Human Beings ◽  
Age Group Classification ◽  
Facial Images

We as human beings can estimate the age of a person based on his facial features but there are situations where there is a need for the computers to determine the age of a person based on the picture or photograph. Here comes the situation to teach a machine to determine the age group of a person with his picture. This is applicable in the fields like determining the age of a criminal with his picture or determining the age of a patient when he has undergone an accident and many other fields. To address this problem the paper proposed a technique of finding the age with Rank Based Edge Texture Unit (RETU). The uniqueness of this method is that it divides the age group into 7 classes i.e. the age groups are 1-10, 11-20, 21-30,31-0,41-50,51-60,>60 . With this method, the results cope up to 97.16% and to slightly increase the efficiency the present paper proposes to add Fuzzy Texton features.

scholarly journals Age Group Classification of Facial Images Using Rank Based Edge Texture Unit (RETU)

2015 ◽  
Vol 45 ◽  
pp. 215-225 ◽  
Author(s):  
Ch. Rajendra Babu ◽  
E. Sreenivasa Reddy ◽  
B. Prabhakara Rao
Keyword(s):  
Group Classification ◽  
Age Group ◽  
Age Group Classification ◽  
Facial Images

scholarly journals Establishment of age group classification for risk stratification in glioma patients

2020 ◽  
Author(s):  
Zhiying Lin ◽  
Runwei Yang ◽  
Yawei Liu ◽  
Kaishu Li ◽  
Guozhong Yi ◽  
...  
Keyword(s):  
Molecular Markers ◽  
Risk Stratification ◽  
Tumor Size ◽  
Age Groups ◽  
Tumor Location ◽  
Group Classification ◽  
Age Group ◽  
Ki 67 ◽  
Who Grade ◽  
Age Group Classification

Abstract Objective: Age is associated with the prognosis of glioma patients, but there is no uniform standard of age-group classification to evaluate the prognosis of glioma patients. In this study, we aimed to establish an age group classification for risk stratification in glioma patients. Methods: A total of 1502 patients diagnosed with gliomas at Nanfang Hospital between 2000 and 2018 were enrolled. The WHO grade of glioma was used as a dependent variable to evaluate the effect of age on risk stratification. The evaluation model was established by logistic regression, and the Akaike information criterion (AIC) value of the model was used to determine the optimal cutoff points for age-classification. The differences in gender, WHO grade, pathological subtype, tumor cell differentiation direction, tumor size, tumor location, and molecular markers between different age groups were analyzed. The molecular markers included GFAP, EMA, MGMT, p53, NeuN, Oligo2, EGFR, VEGF, IDH1, Ki-67, 1p/19q, PR, CD3, H3K27M, and TS. Results: The proportion of men with glioma was higher than that of women with glioma (58.3% vs 41.7%). Analysis of age showed that appropriate classifications of age group were 0-14 years old (pediatric group), 15-47 years old (youth group), 48-63 years old (middle-aged group), and ≥64 years old (elderly group).The proportions of glioblastoma and large tumor size (4-6 cm) increased with age (p = 0.000, p = 0.018, respectively ). Analysis of the pathological molecular markers across the four age groups showed that the proportion of patients with larger than 10% area of Ki-67 expression or positive PR expression increased with age (p = 0.000, p = 0.017, respectively). Conclusion: Age was effective evaluating the risk of glioblastoma in glioma patients. Appropriate classifications of age group for risk stratification were 0-14 years old (pediatric group), 15-47 years old (young group), 48-63 years old (middle age group) and ≥ 64 years old (elderly group). There was significant heterogeneity in WHO grade, tumor size, tumor location and some molecular markers among the four age groups.

Sign in / Sign up

Export Citation Format

Share Document