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Lyana Ismadelani
Department of Physics, Faculty of Science & Technology, Universitas Islam Negeri Sunan Gunung Djati Bandung
Astronomy Earth & Planetary Sciences Education Materials Science & Nanotechnology Physics

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Reconstruction of High Resolution Medical Image Using General Regression Neural Network (GRNN) Yudha Satya Perkasa; Khoerun Nisa Syaja'ah; Lyana Ismadelani; Rena Denya Agustina
Jurnal Penelitian Fisika dan Aplikasinya (JPFA) Vol. 10 No. 2 (2020)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jpfa.v10n2.p137-145

Abstract

Low image resolution has deficiencies in the diagnostic process, this will affect the quality of the image in describing an object in certain tissues or organs, especially in the process of examining patients by doctors or physicians based on the results of imaging medical devices such as CT-scans, MRIs and X-rays. Therefore, this study had developed a General Regression Neural Network (GRNN) type artificial neural network system to reconstruct a medical image so that the image has a significant resolution for the analysis process. The GRNN input layer uses grayscale intensity values with variations in the image position coordinates to produce an optimal resolution. There are four layers in this method, the first is input layer, the second is hidden layer, the third is summation, and the last layer is output. We examined the two parameters with different interval values of 0.2 and of 0.5. The result shows that the interval value of 0.2 is the optimal value to produce an output image that is identical to the input image. This is also supported by the results of the intensity curve of the RGB pattern matched between target and output.
Co-Authors Khoerun Nisa Syaja'ah Rena Denya Agustina Yudha Satya Perkasa, Yudha Satya