New approaches to determine age and gender in image processing techniques using multilayer perceptron neural network

Avuçlu, Emre; Başçiftçi, Fatih

doi:10.1016/j.asoc.2018.05.033

Cited by 15 publications

(7 citation statements)

References 29 publications

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“…The "Relative Attribute SVM + Learning" algorithm is proposed in [18] for the estimation of age based on photographs of human faces; thus, it is considered that the presence of certain facial attributes at different ages keeps a relative order between age-groups. In [19], it is sought to determine the age and gender of a person, and for this, panoramic dental X-ray images are analyzed using image processing and a multilayer perceptron neural network. In another case, [20] proposes a deep hybrid model for classification by age range for human face images, where deep convolutional neural networks are used.…”

Section: Rel Ated Work Smentioning

confidence: 99%

Bruise dating using deep learning

Tirado

Mauricio

2020

Journal of Forensic Sciences

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According to the latest report of the World Health Organization (WHO) [1], domestic violence against women is a global scourge, with a prevalence of physical or sexual abuse against women of just over 30% globally and 29.8% in Latin America (see Figure 1). Some victims of violence report these facts to the authorities, which require forensic clinical services such as bruise dating to support complaints, since the results of dating, used as evidence, can be decisive for justice. However, many of these complaints are filed due to lack of evidence or error in the dating of bruise, which aggravates the consequences of this scourge. Bruise, also known as ecchymosis, is the internal bleeding of the skin due to the rupture of blood vessels, which is caused by an impact with an edgeless object, without tearing or cutting the skin. Therefore, blood escapes from blood vessels close to the surface of the skin being trapped under it [2]. This generates a coloration on the surface of the skin, which shows a series of colors that vary over time and can be visible for up to 30 days from its appearance, so the medical literature reports the use of color scales for dating of an bruise. This procedure consists in determining the age of this bruise; thus, the bruise is visualized in situ or through photographs, and the judgment of an expert is applied [3], who, in general, is a coroner, and its importance lies in being evidence fundamental in a trial for domestic violence or against women. There are studies on the dating of bruise, from the medical and forensic point of view, where the use of histological analysis [4], genetic [5], chromatography [6], and visual inspection techniques are

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Section: Rel Ated Work Smentioning

confidence: 99%

Bruise dating using deep learning

Tirado

Mauricio

2020

Journal of Forensic Sciences

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“…The process of converting input data into a property dataset is called feature extraction (Krishnan, A. & Sreekumar, K. 2014, Avuçlu, E., & Başçiftçi, F. 2018). Feature extraction methods analyze objects and images to extract the most distinctive features representing various object classes.…”

Section: Feature Extractionmentioning

confidence: 99%

Classifying White Blood Cells Using Machine Learning Algorithms

Elen

Turan

2019

Uluslararası Muhendislik Arastirma Ve Gelistirme Dergisi

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Blood and its components have an important place in human life and are the best indicator tool in determining many pathological conditions. In particular, the classification of white blood cells is of great importance for the diagnosis of hematological diseases. In this study, 350 microscopic blood smear images were tested with 6 different machine learning algorithms for the classification of white blood cells and their performances were compared. 35 different geometric and statistical (texture) features have been extracted from blood images for training and test parameters of machine learning algorithms. According to the results, the Multinomial Logistic Regression (MLR) algorithm performed better than the other methods with an average 95% test success. The MLR can be used for automatic classification of white blood cells. It can be used especially as a source for diagnosis of diseases for hematologists and internal medicine specialists.

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“…It can be used in many diagnostic aspects like detection of pulmonary tumor in chest radiographs [ 27 ], in the classification of medical data [ 28 ], in the analysis of microscopic images [ 29 ]. Neural modeling has also been used in determining age and sex [ 30 ] and in clinical diagnostics related to endoscopy [ 31 ]. Neural modeling is also used in the determination of biomarkers [ 32 ] as well as in the detection of brain tumors [ 27 , 28 , 29 , 30 , 31 , 32 , 33 ].…”

Section: Introductionmentioning

confidence: 99%

“…Neural modeling has also been used in determining age and sex [ 30 ] and in clinical diagnostics related to endoscopy [ 31 ]. Neural modeling is also used in the determination of biomarkers [ 32 ] as well as in the detection of brain tumors [ 27 , 28 , 29 , 30 , 31 , 32 , 33 ]. Artificial intelligence methods are also used in the analysis of ECG-electrocardiogram-signal analysis [ 34 , 35 ], in Alzheimer’s and Parkinson’s diagnosis [ 36 , 37 , 38 ], urology [ 39 ], and oncology [ 40 , 41 , 42 , 43 , 44 , 45 ].…”

Section: Introductionmentioning

confidence: 99%

Tooth and Bone Parameters in the Assessment of the Chronological Age of Children and Adolescents Using Neural Modelling Methods

Zaborowicz

Biedziak

Olszewska

et al. 2021

Sensors

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The analog methods used in the clinical assessment of the patient’s chronological age are subjective and characterized by low accuracy. When using those methods, there is a noticeable discrepancy between the chronological age and the age estimated based on relevant scientific studies. Innovations in the field of information technology are increasingly used in medicine, with particular emphasis on artificial intelligence methods. The paper presents research aimed at developing a new, effective methodology for the assessment of the chronological age using modern IT methods. In this paper, a study was conducted to determine the features of pantomographic images that support the determination of metric age, and neural models were produced to support the process of identifying the age of children and adolescents. The whole conducted work was a new methodology of metric age assessment. The result of the conducted study is a set of 21 original indicators necessary for the assessment of the chronological age with the use of computer image analysis and neural modelling, as well as three non-linear models of radial basis function networks (RBF), whose accuracy ranges from 96 to 99%. The result of the research are three neural models that determine the chronological age.

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New approaches to determine age and gender in image processing techniques using multilayer perceptron neural network

Cited by 15 publications

References 29 publications

Bruise dating using deep learning

Bruise dating using deep learning

Classifying White Blood Cells Using Machine Learning Algorithms

Tooth and Bone Parameters in the Assessment of the Chronological Age of Children and Adolescents Using Neural Modelling Methods

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