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

• Published in: International journal of advanced computer science & applications Vol. 13; no. 7
• Main Authors: Poojary, Manjula, Srinivas, Yarramalle
• Format: Journal Article
• Language: English
• Published: West Yorkshire Science and Information (SAI) Organization Limited 01.01.2022
• Subjects: Algorithms; Artificial neural networks; Bivariate analysis; Cameras; Classifiers; Electromagnetic radiation; Energy; Energy distribution; Energy levels; Feature extraction; Heuristic methods; Human body; Image processing; Image segmentation; Medical imaging; Methodology; Organs; Particle swarm optimization; Probabilistic models; Radiation; Support vector machines; Thermal radiation
• ISSN: 2158-107X; 2156-5570; 2156-5570
• DOI: 10.14569/IJACSA.2022.0130770

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%.