scholarly journals Machine Learning Techniques for Human Age and Gender Identification Based on Teeth X-Ray Images

2022 ◽  
Vol 2022 ◽  
pp. 1-14
Author(s):  
K. C. Santosh ◽  
Nijalingappa Pradeep ◽  
Vikas Goel ◽  
Raju Ranjan ◽  
Ekta Pandey ◽  
...  
Keyword(s):  
Machine Learning ◽  
Digital Images ◽  
Automated System ◽  
Machine Learning Techniques ◽  
Human Beings ◽  
Age And Gender ◽  
X Ray ◽  
Learning Techniques ◽  
Gender And Age ◽  
And Gender

The use of digital medical images is increasing with advanced computational power that has immensely contributed to developing more sophisticated machine learning techniques. Determination of age and gender of individuals was manually performed by forensic experts by their professional skills, which may take a few days to generate results. A fully automated system was developed that identifies the gender of humans and age based on digital images of teeth. Since teeth are a strong and unique part of the human body that exhibits least subject to risk in natural structure and remains unchanged for a longer duration, the process of identification of gender- and age-related information from human beings is systematically carried out by analyzing OPG (orthopantomogram) images. A total of 1142 digital X-ray images of teeth were obtained from dental colleges from the population of the middle-east part of Karnataka state in India. 80% of the digital images were considered for training purposes, and the remaining 20% of teeth images were for the testing cases. The proposed gender and age determination system finds its application widely in the forensic field to predict results quickly and accurately. The prediction system was carried out using Multiclass SVM (MSVM) classifier algorithm for age estimation and LIBSVM classifier for gender prediction, and 96% of accuracy was achieved from the system.

scholarly journals Novel approach to determine age and gender from wrist x-ray images

2021 ◽  
Vol 12 (3) ◽  
pp. 5215-5229
Author(s):  
Santosh K C Et. al.
Keyword(s):  
Random Forest ◽  
Decision Tree ◽  
Classification Algorithm ◽  
Automated System ◽  
Growth Stages ◽  
Age And Gender ◽  
X Ray ◽  
Gender And Age ◽  
And Gender ◽  
Time Accuracy

Human gender and age prediction in field of forensic department is a very important and crucial stage in means of criminal and judicial law. Human identification is essential when it required for recognising a body in case of mass disaster and natural disaster like earth quake, floods, tsunamis, hurricanes and other geological process that causes huge damage for mankind and loss of life. Human bones during the growth stages undergo few substantial changes of size and shapes. In diagnosing growth of bones, x ray images are frequently used. Hand x ray images in particular has been chosen as a part of x ray imaging, since hand has more unique features and more number of parts. Manual technique of identification is also attainable, but this process can be adopted when medical practitioners, assistants and basic tools are available. Manual method can be carried out based on the availability of bone like skull, long bones, short bones, hand, pelvis bone etc. It requires ample time to process the accurate outcome of the available samples. Hence hand operated technique is not feasible for identification. A machine driven automated system for gender and age identification is essential to overcome the flaws occurred in manual technique. This would facilitate better outcome in lesser time, without intervention of labour and also enables quantitative and accurate assessments. In the proposed system, we have identified most important features from wrist bone which contributes in age and gender identification. Main aim of our study is to identify gender and estimation of age of Middle East population of Karnataka state in India by analysing digital images of wrist bone. Random forest classification algorithm is used to deploy this system by considering 76 male samples and 50 female samples in total 126 wrist radiographs of age group between 06 to 78 years old. Random forest classifier belongs to decision tree family, each decision tree when executed may tends to overfit that training data, but random forest avoids this over fitting problems and it will try to capture maximum amount of pattern. Since multiple decision trees are implemented in RFC, this makes it a power full classification algorithm that will predict results with higher accuracy most of the time. Accuracy of 97% is achieved in the present work for age and gender prediction.

scholarly journals A Heuristic Model for Predicting Human Fall Detection using Machine Learning Techniques

2020 ◽  
Vol 9 (2) ◽  
pp. 119-121
Keyword(s):  
Machine Learning ◽  
Health Problem ◽  
Fall Detection ◽  
Machine Learning Techniques ◽  
Technological Advancement ◽  
Heuristic Model ◽  
Human Beings ◽  
Machine Learning Classifiers ◽  
Learning Techniques ◽  
Smart Health

It is very obvious that human fall due to unconsciousness is a very common health problem in every human being. With the evolution of many smart health devices, we should contribute the technological advancement of machine learning into it. Different techniques are already used in order to detect human fall detection in human beings. In this paper we have studied the patterns of falling of human through the fall detection dataset while this human was performing various motions. By understanding all these we have generated the prediction protocol which estimates the fall of a person using fall detection dataset. Machine Learning classifiers were used to predict the human fall and a comparative study of various algorithms used was developed to find out the best classifier.

Chest X-Ray Image-Based Testing Using Machine Learning Techniques

Covid-19 ◽  
2021 ◽  
pp. 241-278
Author(s):  
Parag Verma ◽  
Ankur Dumka ◽  
Alaknanda Ashok ◽  
Amit Dumka ◽  
Anuj Bhardwaj
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
X Ray ◽  
Learning Techniques ◽  
Chest X Ray

scholarly journals Identification of Pneumonia Disease Applying an Intelligent Computational Framework Based on Deep Learning and Machine Learning Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Yar Muhammad ◽  
Mohammad Dahman Alshehri ◽  
Wael Mohammed Alenazy ◽  
Truong Vinh Hoang ◽  
Ryan Alturki
Keyword(s):  
Machine Learning ◽  
Medical Image Analysis ◽  
Machine Learning Techniques ◽  
World Health ◽  
X Ray ◽  
Expert Radiologist ◽  
Learning Techniques ◽  
Effective Manner ◽  
The World ◽  
Chest X Ray

Pneumonia is a very common and fatal disease, which needs to be identified at the initial stages in order to prevent a patient having this disease from more damage and help him/her in saving his/her life. Various techniques are used for the diagnosis of pneumonia including chest X-ray, CT scan, blood culture, sputum culture, fluid sample, bronchoscopy, and pulse oximetry. Medical image analysis plays a vital role in the diagnosis of various diseases like MERS, COVID-19, pneumonia, etc. and is considered to be one of the auspicious research areas. To analyze chest X-ray images accurately, there is a need for an expert radiologist who possesses expertise and experience in the desired domain. According to the World Health Organization (WHO) report, about 2/3 people in the world still do not have access to the radiologist, in order to diagnose their disease. This study proposes a DL framework to diagnose pneumonia disease in an efficient and effective manner. Various Deep Convolutional Neural Network (DCNN) transfer learning techniques such as AlexNet, SqueezeNet, VGG16, VGG19, and Inception-V3 are utilized for extracting useful features from the chest X-ray images. In this study, several machine learning (ML) classifiers are utilized. The proposed system has been trained and tested on chest X-ray and CT images dataset. In order to examine the stability and effectiveness of the proposed system, different performance measures have been utilized. The proposed system is intended to be beneficial and supportive for medical doctors to accurately and efficiently diagnose pneumonia disease.

scholarly journals Detecting and Classifying Pests in Crops Using Proximal Images and Machine Learning: A Review

AI ◽  
2020 ◽  
Vol 1 (2) ◽  
pp. 312-328
Author(s):  
Jayme Garcia Arnal Barbedo
Keyword(s):  
Machine Learning ◽  
Digital Images ◽  
Research Effort ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Actual State ◽  
Considerable Research ◽  
Learning Techniques ◽  
Pest Detection ◽  
The Subject

Pest management is among the most important activities in a farm. Monitoring all different species visually may not be effective, especially in large properties. Accordingly, considerable research effort has been spent towards the development of effective ways to remotely monitor potential infestations. A growing number of solutions combine proximal digital images with machine learning techniques, but since species and conditions associated to each study vary considerably, it is difficult to draw a realistic picture of the actual state of the art on the subject. In this context, the objectives of this article are (1) to briefly describe some of the most relevant investigations on the subject of automatic pest detection using proximal digital images and machine learning; (2) to provide a unified overview of the research carried out so far, with special emphasis to research gaps that still linger; (3) to propose some possible targets for future research.

Discovering Knowledge Hidden in Big Data from Machine-Learning Techniques

2017 ◽  
pp. 167-183 ◽  
Author(s):  
Adiraju Prashantha Rao
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Decision Makers ◽  
Machine Learning Techniques ◽  
Human Beings ◽  
Raw Data ◽  
Learning Techniques ◽  
Data Analytic ◽  
Information Growth

As the speed of information growth exceeds in this new century, excessive data is making great troubles to human beings. However, there are so much potential and highly useful values hidden in the huge volume of data. Big Data has drawn huge attention from researchers in information sciences, policy and decision makers in governments and enterprises. Data analytic is the science of examining raw data with the purpose of drawing conclusions about that information. Data analytics is about discovering knowledge from large volumes data and applying it to the business. Machine learning is ideal for exploiting the opportunities hidden in big data. This chapter able to discover and display the patterns buried in the data using machine learning.

Impact Analysis of Machine Learning Techniques for COVID-19 Diagnosis: A Critical Review

2021 ◽  
Vol 12 (2) ◽  
pp. 510-516
Author(s):  
Anshul, Et. al.
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Severe Acute Respiratory Syndrome ◽  
Impact Analysis ◽  
Machine Learning Techniques ◽  
X Ray ◽  
Learning Techniques ◽  
The World ◽  
Almost All

COVID-19 virus belongs to the severe acute respiratory syndrome (SARS) family raised a situation of health emergency in almost all the countries of the world. Numerous machine learning and deep learning based techniques are used to diagnose COVID positive patients using different image modalities like CT SCAN, X-RAY, or CBX, etc. This paper provides the works done in COVID-19 diagnosis, the role of ML and DL based methods to solve this problem, and presents limitations with respect to COVID-19 diagnosis.

Sign in / Sign up

Export Citation Format

Share Document