Automatic Computerized Diagnostic Tool for Down Syndrome Detection in Fetus

2019 ◽  
pp. 226-243
Author(s):  
Michael Dinesh Simon ◽  
Kavitha A. R.
Keyword(s):  
Down Syndrome ◽  
Diagnostic Tool ◽  
Chromosome Abnormality ◽  
Genetic Disorder ◽  
Nasal Bone ◽  
Training Phase ◽  
Phase Detection ◽  
Femur Length ◽  
Testing Phase ◽  
Two Phases

Down syndrome is a genetic disorder and the chromosome abnormality observed in humans that can cause physical and mental abnormalities. It can never be cured or rectified. Instead it has to be identified in the fetus and prevented from being born. Many ultrasonographic markers like nuchal fold, nasal bone hypoplasia, femur length, and EIF are considered to be the symptoms of Down syndrome in the fetus. This chapter deals with the creation of automatic and computerized diagnostic tool for Down syndrome detection based on EIF. The proposed system consists of two phases: 1) training phase and 2) testing phase. In training phase, the fetal images with EIF and Down syndrome is analyzed and characteristics of EIF are collected. In testing phase, detection of Down syndrome is performed on the fetal image with EIF based on the knowledge cluster obtained using ESOM. The performance of the proposed system is analyzed in terms of sensitivity, accuracy, and specificity.

scholarly journals Ultrasonic Detection of Down Syndrome Using Multiscale Quantiser with Convolutional Neural Network

2021 ◽  
Author(s):  
Michael Dinesh Simon ◽  
A.R. Kavitha
Keyword(s):  
Neural Network ◽  
Down Syndrome ◽  
Convolutional Neural Network ◽  
Ultrasonic Method ◽  
Nasal Bone ◽  
Diagnostic Process ◽  
Training Phase ◽  
Ultrasonic Detection ◽  
Extra Copy ◽  
Testing Phase

Down Syndrome is a genetic condition that occurs when there is an extra copy of a chromosome 21 in the newly formed fetus. EIF is observed as one of the possible symptoms of DS. But in comparison to the other symptoms like nasal bone hypoplasia, increased thickness in the nuchal fold, EIF is very much less prone to DS. Hence, recommending the pregnant women with EIF to undergo the diagnostic process like amniocentesis, CVS and PUBS is not always a right choice as these diagnostic processes suffer serious drawbacks like miscarriage, uterine infections. This chapter “Ultrasonic Detection of Down Syndrome Using Multiscale Quantiser With Convolutional Neural Network” presents a new ultrasonic method to detect EIF that can cause DS. Ultrasonic Detection of Down Syndrome Using Multiscale Quantiser with Convolutional Neural Network entails two stages namely i) training phase and ii) testing phase. Training phase aims at learning the features of EIF that can cause DS whereas testing phase classifies the EIF into DS positive or DS negative based on the knowledge cluster formed during the training phase. A new algorithm Multiscale Quantiser with the convolutional neural network is used in the training phase. Enhanced Learning Vector Classifier is used in the testing phase to differentiate the normal EIF from EIF causing DS. The performance of the proposed system is analysed in terms of sensitivity, accuracy and specificity.

scholarly journals Content Based Confidentiality Detection Method for Data Leakage Prevention

2019 ◽  
Vol 8 (6S3) ◽  
pp. 2080-2087
Keyword(s):  
Detection Method ◽  
Public Organizations ◽  
Training Phase ◽  
Internal Factors ◽  
Testing Phase ◽  
Confidential Data ◽  
Prevention System ◽  
Private And Public ◽  
Two Phases

protecting confidential data became a challenge for all private and public organizations. According to Gartner report, the majority of data leakages in organizations are due to internal factors. Data Leakage Prevention Systems can protect monitor and identify the confidential data at-rest, inuse and in-motion. This paper presents a Data Leakage Prevention system, to prevent confidential data from leakages using the Term Based Confidentiality Detection Method .The proposed method consists of two phases: training and testing phase. The training phase identifies confidential terms from the documents and testing phase detects the confidentiality of the document.

scholarly journals Penentuan Aspek Implisit dengan Ekstraksi Knowledge Berbasis Rule pada Ulasan Bahasa Indonesia (Determination of Implicit Aspects with Rule Based Knowledge Extraction in Indonesian Reviews)

2020 ◽  
Vol 9 (1) ◽  
pp. 35-44
Author(s):  
Yuliana Setiowati ◽  
Fitri Setyorini ◽  
Afrida Helen
Keyword(s):  
Knowledge Extraction ◽  
Training Phase ◽  
Threshold Values ◽  
Rule Based ◽  
New Approach ◽  
Word Similarity ◽  
Testing Phase ◽  
Two Phases ◽  
The Relationship

Determination of implicit aspects is one of the important things in opinion sentences. This study proposes a new approach for developing rule-based knowledge by forming a relation between opinion words with aspect categories. The relationship is obtained from the combination of rules, based on Opinion Word Similarity (OWS). Evaluation for rule-based knowledge extraction is in the form of threshold values of frequency and confidence to produce the best precision, recall, and f-measure values. The knowledge extraction consists of two phases: training phase and testing phase. The training phase is described as the process to extract rule-based knowledge. The testing phase is described as the process to obtain the implicit aspects of opinion sentences by referring to rule-based knowledge. To extract rule-based knowledge on user reviews, it is necessary to identify opinion sentences with explicit aspects and get pairs of aspects and words of opinion with rules generated from regular expressions. The evaluation res ult of rule-based knowledge with confidence using OWS showed better results compared to rule-based knowledge without using OWS. By using OWS, precision value increased by 0.25%, recall value increased by 1.15%, and precision value increased by 0.83%.

UNIVERSAL WRITING MODEL FOR RECOVERY OF WRITING SEQUENCE OF STATIC HANDWRITING IMAGES

2005 ◽  
Vol 19 (05) ◽  
pp. 603-630 ◽  
Author(s):  
KAI KWONG LAU ◽  
PONG CHI YUEN ◽  
YUAN YAN TANG
Keyword(s):  
Handwriting Recognition ◽  
Ground Truth ◽  
Distribution Functions ◽  
Training Phase ◽  
Two Dimensional ◽  
Reconstruction Process ◽  
Testing Phase ◽  
Static Image ◽  
Two Phases ◽  
Handwritten Signature

Online features have been proven to be more robust information for handwriting recognition than an offline static image due to dynamic aspects, such as the writing sequence of strokes. The estimation of temporal information from a static image becomes an important issue. This paper presents a new statistical method to reconstruct the writing order of a handwritten signature from a two-dimensional static image. The reconstruction process consists of two phases, namely the training phase and the testing phase. In the training phase, the writing order with other attributes, such as length and direction, are extracted and analyzed from a set of training online handwritten signatures. A Universal Writing Model (UWM), which consists of a set of distribution functions, is then constructed. In the testing phase, the UWM is applied to reconstruct the writing order of an offline signature. 300 offline signatures with ground truth are used for evaluation. Experimental results show that about one-eighth of the reconstructed writing sequences are the same as the actual writing sequences.

Orthodontic Aspects on the Chronological and Dental Age in Children with Down Syndrome

2018 ◽  
Vol 69 (1) ◽  
pp. 208-213
Author(s):  
Mariana Pacurar ◽  
Bogdan Dragomir ◽  
Alina Silvana Szalontay ◽  
Cristian Romanec
Keyword(s):  
Down Syndrome ◽  
Emotional Disorders ◽  
Metabolic Diseases ◽  
Genetic Disorder ◽  
Signs And Symptoms ◽  
Deciduous Teeth ◽  
Healthy Children ◽  
Dental Maturation ◽  
Children With Down Syndrome ◽  
The Family

Genetics is a key discipline in medicine, but also a clinical discipline with medical and social implications. The interest in reducing the number of genetic disorders and recognizing the risk of them repeating when a family confronts itself with a genetic anomaly becomes more and more important in the hierarchy of prophylactic emergencies. Presenting themselves as metabolic diseases (monogenic mutations) or malformations (polygenic and multifactorial heredity) because of their frequency, these disorders position themselves on an ascendant curve. They become difficult to deal with for the society, for the family and for the interested individual and cause emotional disorders. The Down syndrome is the most frequent type of genetic disorder. It is characterized by a specific set of signs and symptoms. People with Down syndrome require special medical care that, apart from the family, must include a team of doctors of various specializations and also a dentist. They are predisposed to hearing and sight disorders and thyroid problems as well. In 50% of the cases there are also anomalies of the heart, and the risk of leukaemia is 20 times higher. Some of them even develop an Alzheimer type dementia during their life. The people with Down syndrome can have an average IQ up to a moderate form of handicap. In particular, the studies on Down syndrome in dentistry are quite frequent, but they focus more on cavities, periodontal disease and hypodontia. In spite of this, the connection of Down syndrome and dental eruption is less studied. Consequently, the present study is intended to fill this missing part from the specialized literature, focusing on the relation between the Down syndrome and the chronological and dental ages in children. The health of the oral cavity is neglected in these patients, their parents focusing more on the treatment of the other systemic disorders of their children; the lack of interest is reflected in their poor oral hygiene.The trial group included 94 children with mixt dentition, aged between 6 and 12, divided as follows: 36 children with Down syndrome enrolled at the Educational Centre for Inclusive Education no. 1 of Tg. Mures and Alpha Transilvana Foundation. The chronology and the eruption sequences are subjected to certain variations and they are influenced by the presence of cavities, the premature loss or, on the contrary, the prolonged retention of deciduous teeth as well as dental anchylosis. Dental maturation is less subjected to variations, as it is a progressive, continuous and cumulative process. The presence of Down syndrome in children generates a delay in teeth eruption by 1.27 years compared to the data identified in the specialized literature and to the information obtained on the healthy children included in the study.

Fetal Nasal Bone Assessment in First Trimester Down Syndrome Screening

2008 ◽  
Vol 24 (1) ◽  
pp. 61-66 ◽  
Author(s):  
Recep Has ◽  
Ibrahim Kalelioglu ◽  
Atil Yuksel ◽  
Lemi Ibrahimoglu ◽  
Hayri Ermis ◽  
...  
Keyword(s):  
Down Syndrome ◽  
Nasal Bone ◽  
First Trimester ◽  
Fetal Nasal Bone ◽  
Bone Assessment ◽  
Down Syndrome Screening

Practice Tips for Measuring the Nuchal Translucency

Ultrasound ◽  
2008 ◽  
Vol 16 (4) ◽  
pp. 220-225 ◽  
Author(s):  
Debbie Nisbet
Keyword(s):  
Down Syndrome ◽  
Nasal Bone ◽  
Nuchal Translucency ◽  
Operator Technique ◽  
Increased Risk ◽  
Fetal Nasal Bone ◽  
Form Part ◽  
The Uk ◽  
Bone Assessment ◽  
Down Syndrome Screening

In some countries, measurement of nuchal translucency (NT) is incorporated into national antenatal screening programmes to help detect pregnancies at increased risk of Down syndrome. Accurate measurement of the NT requires a specific technique. This article is an illustrated practical guide outlining the steps required for measuring the NT; it provides useful tips for improving operator technique and advises how to avoid common pitfalls. Although fetal nasal bone assessment does not currently form part of official Down syndrome screening programmes (in Australia or the UK), it is included here as debate about its usefulness continues.

scholarly journals Writer Identification Based on Hand Writing using Artificial Neural Network

2021 ◽  
Vol 3 (2) ◽  
pp. 257-264
Author(s):  
Rosalia Arum Kumalasanti ◽  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Social Interaction ◽  
Social Relations ◽  
Image Data ◽  
Training Phase ◽  
Testing Phase ◽  
Training Stage ◽  
Artificial Neural ◽  
Hand Writing

Humans are social beings who depend on social interaction. Social interaction that is often used is communication. Communication is one of the bridges to connect social relations between humans. Communication can be delivered in two ways, namely verbal or nonverbal. Handwriting is an example of nonverbal communication using paper and writing utensils. Each individual's writing has its own uniqueness so that handwriting often becomes the character or characteristic of the author. The handwriting pattern usually becomes a character for the writer so that people who recognize the writing will easily guess the ownership of the related handwriting. However, handwriting is often used by irresponsible people in the form of handwriting falsification. The acts of writing falcification often occur in the workplace or even in the field of education. This is one of the driving factors for creating a reliable system in tracking someone's handwriting based on their ownership. In this study, we will discuss the identification of a person's handwriting based on their ownership. The output of this research is in the form of ID from the author and accuracy in the form of percentage of system reliability in identifying. The results of this study are expected to have a good impact on all parties, in order to minimize plagiarism. Identification of handwriting to be built consists of two main processes, namely the training phase and the testing phase. At the training stage, the handwritten image is subjected to several processes, namely threshold, wavelet conversion, and then will be trained using the Backpropagation Artificial Neural Network. In the testing phase, the process is the same as in the training phase, but at the end of the process, a comparison will be made between the image data that has been stored during training with a comparison image. Backpropagation ANN can work optimally if it is trained using input data that has determined the size, learning rate, parameters, and the number of nodes on the network. It is expected that the offered method can work optimally so that it produces an accurate percentage in order to minimize handwriting falcification.

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