Angular Coherence Observation in Fingerprints and Lungs for Fingerprint Classification and COVID-19 Differentiation

Hemad Heidari Jobaneh
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Hemad Heidari Jobaneh: Independent Scholar, Iran

European Journal of Electrical Engineering and Computer Science, 2020, vol. 4, issue 4

Abstract: Feature extraction and lung detection are critical phases for COVID-19 detection. Hence, the features by which normal lungs and abnormal lungs can be differentiated are significantly important. In this paper, the x-ray images are enhanced and the corresponding angles, coming from ribs, are extracted as the major features. According to the behavior of the angles, the image is bisected in order to evaluate each lung individually. The new definition of normal lungs is proposed so as to discriminate normal lungs from COVID-19 lungs. Considering the definition, the right and the left lungs are cropped from the main image. Subsequently, the Histogram of Oriented Gradient (HOG) features are extracted from the cropped images. Two neural networks with the same topology are trained by the features. First, one of the neural networks is trained by cropped images. Second, another neural network is trained by HOG features obtained from the cropped images. The simulation is performed by MATLAB and the database is comprised of 522 images and 96% accuracy is obtained. Furthermore, a novel method by which fingerprints are classified in eight categories is proposed in this paper. In fact, because of inevitable rotation, brought about during data acquisition procedure in fingerprints, the feature extraction procedure might be afflicted with the rotation. Hence, a new approach is suggested so that the rotation is rectified prior to the feature extraction process. From the enhanced images of fingerprints, the angles of ridges are calculated. According to the extracted angles, new points, called Origin Points, are mentioned as the origins around which decisive blocks are cropped. For each block, a Fourier series model is calculated so as to form a training data for the classifier. The classifier chosen is a Generalized Regression Neural Network (GRNN). FVC2004 is utilized for both training and test phases and 98.2% accuracy is achieved.

Keywords: Angular Coherence; COVID-19 differentiation; Fingerprint Classification; Neural Network (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:epw:ejece0:v:4:y:2020:i:4:id:19235

DOI: 10.24018/ejece.2020.4.4.235

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