Could Aura Images Can Be Treated as Medical Images?

Rajesh, R.; Priya, B. Shanmuga; Kumar, J. Satheesh; Arulmozhi, V.

doi:10.1007/978-3-642-25453-6_15

Cited by 7 publications

(3 citation statements)

References 6 publications

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“…They stated that humans consist of many mysteries so that research on aura images can understand the individual's state of mind and health conditions. Furthermore, Rajesh et al [19] through their study used aura images for medical diagnosis. In this case, experiments were carried out with 40 normal and abnormal subjects through a method where aura images were captured by mapping the energy of the five hand meridian points [20].…”

Section: Introductionmentioning

confidence: 99%

“…In this case, experiments were carried out with 40 normal and abnormal subjects through a method where aura images were captured by mapping the energy of the five hand meridian points [20]. In addition, Rajesh [14] utilized the support vector machine (SVM) method to study patterns to recognize image textures based on the gray level aura matrix (GLAM). The success of this system resulted in an average value of 92%.…”

Section: Introductionmentioning

confidence: 99%

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Aura detection using thermal camera with convolutional neural network method for mental health diagnosis

Ariswati

Soetjiatie

2023

IJEECS

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Mental health is an important aspect in realizing overall health. For this reason, non-invasive medical equipment is needed for people with mental disorders. This study aimed to create a psychological health diagnostic tool by detecting auras using a thermal camera from facial objects. The contribution of this study is that the tool can detect the patient's aura without physical contact so that the patient is more comfortable and does not feel invaded. This research designed a system for detecting electromagnetic wave radiation energy emitted by the body using a thermal camera. Face detection in the input image was performed using a convolutional neural network (CNN) model single shot multibox detector (SSD), which is one of the CNN models that implements a bounding box to estimate the localization of detected objects. In this case, system testing was used to evaluate the performance of the CNN system algorithm for aura detection in terms of color (main color or average color). The results obtained were detectibility by 80%, selectivity by 88.88%, precision by 70%, sensitivity by 87.5%, and accuracy by 63.63%. The design of the aura detection system in this study will make it easier for psychiatrists and psychologists to help make a noninvasive diagnosis.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Aura detection using thermal camera with convolutional neural network method for mental health diagnosis

Ariswati

Soetjiatie

2023

IJEECS

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“…[9,10,11,12]. It is also presented in the literature that parametric model-based approach is more adaptive compared to the nonparametric model [13,14,15,16]. The latest advancement in the area of computer communication technologies led towards the development of machine learning technique and thereby literature has been driven in this direction of research, using machine learning technique such as Convolution Neural Network (CNN), Artificial Neural Network (ANN), Bayesian belief network etc.…”

Section: Introductionmentioning

confidence: 99%

A Novel Methodology for Disease Identification Using Metaheuristic Algorithm and Aura Image

Poojary¹,

Srinivas²

2022

IJACSA

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Every human has a specific Aura. Every organism in the human body emits energy comprising of Ultra Violet radiation, thermal radiation, and electromagnetic radiation. These energy levels help to underline the physical health inside the human body. In general, these energy levels are called Aura. In order to capture the energy levels, specific cameras like Kirlian are used. These cameras try to capture the energy distribution and map them to the individual organs of the human body. In this article, we present a methodology using Image processing techniques, where Bivariate Gaussian Mixture Model (BGMM) is considered as a classifier to identify the diseases in humans based on the energy distribution. In this article, we have considered five different categories of diseased organs that are identified based on the energy distribution. The preprocessing is subjected to the morphological technique and Particle Swarm Optimization (PSO) algorithm is considered for feature extraction. The segmentation process is carried out using the feature extracted and training is carried out using the BGMM classifier. The result obtained is summarized using various other methods like Support Vector Machine (SVM), Artificial Neural Network (ANN), and Multiclass SVM (MSVM). The results showcase that the proposed methodology exhibits recognition accuracy at 90%.

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Preliminary study of Kirlian image in digital electrophotonic imaging and its applications

Alipal

Lee

Farzamnia

2017

2017 IEEE 2nd International Conference on Automatic Control and Intelligent Systems (I2CACIS)

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Could Aura Images Can Be Treated as Medical Images?

Cited by 7 publications

References 6 publications

Aura detection using thermal camera with convolutional neural network method for mental health diagnosis

Aura detection using thermal camera with convolutional neural network method for mental health diagnosis

A Novel Methodology for Disease Identification Using Metaheuristic Algorithm and Aura Image

Preliminary study of Kirlian image in digital electrophotonic imaging and its applications

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