Backpropagation Neural Network for Sex Determination from Patella in Forensic Anthropology

Afrianty, Iis; Nasien, Dewi; Kadir, Mohammed Rafiq Abdul; Haron, Habibollah

doi:10.1007/978-3-642-41674-3_103

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…Kemudian hitung koreksi bobot yang digunakan untuk memperbaiki nilai vjk (Persamaan 11). jk = αδj xi (11) Hitung juga koreksi bobot bias akan digunakan untuk memperbaiki nilai voj (Persamaan 12).…”

Section: J = F (Z_in J)unclassified

Implementation of Backpropagation Neural Network to Detect Suspected Lung Disease

Syafria

Iqbal²,

Budianita³

et al. 2018

IJAIDM

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Many People were less concerned with lung health, it caused people identified as suffering from lung diseases. Early symptoms that often appear was cough that took a long time and could be the beginning of more severe disease. Therefore it was necessary to create application that could detect suspected person contracted lung disease. The applications were made by using artificial neural network with Backpropagation with initial input data, symptoms by patients of lung diseases. The symptoms were 22, and kind of lung diseases as a diagnosis were asthma, pneumonia, pulmonary tuberculosis and lung cancer. It used medical records of lung disease as much as 110 data. Network training uses 3 different architectures [input neurons ; hidden neurons ; output neurons], liked [22; 22 ; 2], [22 ; 33 ; 2] and [22 ; 43 ; 2]. Performance measurement was carried out on two types of training data and testing data distribution, namely comparison 90:10 and 80:20. The Parameters values were used namely learning rate 0.1, 0.3, 0.5, 0.7 and 0.9. The number of epoch was used, that is 15 epoch, 25 epoch and 35 epoch.Based on the tests performed, it was obtained an accuracy system on the 90:10 data comparison of 82% and the 80:20 data ratio of 82% as well. Thus, backpropagation method could be applied in detecting suspected lung diseases.

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Section: J = F (Z_in J)unclassified

Implementation of Backpropagation Neural Network to Detect Suspected Lung Disease

Syafria

Iqbal²,

Budianita³

et al. 2018

IJAIDM

Self Cite

View full text Add to dashboard Cite

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“…Biological age determination [8,9,[16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] 2. Sex determination [35][36][37][38][39][40][41][42][43] 3. Automatized 3D cephalometric landmark annotation [44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61] 4.…”

Section: Introductionmentioning

confidence: 99%

Use of Advanced Artificial Intelligence in Forensic Medicine

Thurzo¹,

Kosnáčová²,

Kurilová³

et al. 2021

Preprint

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Three-dimensional convolutional neural networks (3D CNN) as a type of artificial intelligence (AI) are powerful in image processing and recognition using deep learning to perform generative and descriptive tasks. The advantage of CNN compared to its predecessors is that it automatically detects the important features without any human supervision. 3D CNN are used to extract features in three dimensions where input is a 3D volume or a sequence of 2D pictures e.g., slices in a cone-beam computer tomography scan (CBCT). The main aim of this article was to bridge interdisciplinary cooperation between forensic medical experts and deep learning engineers. With emphasis activating clinical forensic experts in the field with possibly basic knowledge of advanced artificial intelligence techniques with interest in its implementation in their efforts to advance the forensic research further. This paper introduces a novel workflow of 3D CNN analysis of full-head CBCT scans. Authors explore and present 3D CNN method for forensic research design concept in five perspectives: (1) sex determination, (2) biological age estimation, (3) 3D cephalometric landmark annotation, (4) growth vectors prediction, (5) facial soft-tissue estimation from the skull and vice versa. In conclusion, 3D CNN application can be a watershed moment in forensic medicine, leading to unprecedented improvement of forensic analysis workflows based on 3D neural networks.

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Use of Advanced Artificial Intelligence in Forensic Medicine, Forensic Anthropology and Clinical Anatomy

et al. 2021

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Three-dimensional convolutional neural networks (3D CNN) of artificial intelligence (AI) are potent in image processing and recognition using deep learning to perform generative and descriptive tasks. Compared to its predecessor, the advantage of CNN is that it automatically detects the important features without any human supervision. 3D CNN is used to extract features in three dimensions where input is a 3D volume or a sequence of 2D pictures, e.g., slices in a cone-beam computer tomography scan (CBCT). The main aim was to bridge interdisciplinary cooperation between forensic medical experts and deep learning engineers, emphasizing activating clinical forensic experts in the field with possibly basic knowledge of advanced artificial intelligence techniques with interest in its implementation in their efforts to advance forensic research further. This paper introduces a novel workflow of 3D CNN analysis of full-head CBCT scans. Authors explore the current and design customized 3D CNN application methods for particular forensic research in five perspectives: (1) sex determination, (2) biological age estimation, (3) 3D cephalometric landmark annotation, (4) growth vectors prediction, (5) facial soft-tissue estimation from the skull and vice versa. In conclusion, 3D CNN application can be a watershed moment in forensic medicine, leading to unprecedented improvement of forensic analysis workflows based on 3D neural networks.

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Backpropagation Neural Network for Sex Determination from Patella in Forensic Anthropology

Cited by 3 publications

References 26 publications

Implementation of Backpropagation Neural Network to Detect Suspected Lung Disease

Implementation of Backpropagation Neural Network to Detect Suspected Lung Disease

Use of Advanced Artificial Intelligence in Forensic Medicine

Use of Advanced Artificial Intelligence in Forensic Medicine, Forensic Anthropology and Clinical Anatomy

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