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A Study on the Improvement of Security Image Analysis Capability Using Artificial Intelligence

Seng-Phil Hong ; AI Advanced School, aSSiST (a Seoul School of Integrated Science & Technologies), 46 Ewhayeodae 2-gil, Fintower, Sinchon-ro, Seodaemun-gu, Seoul, 03767, Korea
Debnath Bhattacharyya orcid id ; Department of Computer Science and Engineering, Koneru Laksmaiah Education Foundation, Vaddeswaram, Guntur 522302, India

Puni tekst: engleski pdf 885 Kb

str. 598-604

preuzimanja: 63



The mining of patient data in the health care industry is becoming an increasingly important field because of the direct effect it has on the lives of patients. In the field of medicine, one use of data mining is the early diagnosis of medical diagnostic conditions. However, extracting information from medical records is a laborious process that involves a lot of time and effort. Communities that are dominated by females have an elevated risk of developing breast cancer. Even though mammography is one of the most common ways to use computer-assisted diagnostics, there is still a chance that breast cancer will not be found even if it is one of the most common ways to find and screen for the disease. This indicates that just thirty percent of breast cancers are diagnosed at the appropriate time. Digital image pre-processing includes grayscale-to-binary conversion, noise reduction, and character separation. Most picture recognition algorithms employ statistical, syntactic, and template matching. Neural networks and support vector machines have enabled recent photo identification advances. This article discusses the second stage of the pre-processing procedure, which is adding a filter to the image after it has been segmented in order to make it seem more appealing. It works to identify the area of interest and improve the image by removing the breast border in order to apply filtering algorithms. The breast image's edge is reconstructed using morphological processes in the segmentation method that has been proposed, and breast masses are found by subtracting the two images. In addition, a modified bi-level histogram and homomorphic filters were used in order to improve the image's quality by reducing noise and enhancing contrast.

Ključne riječi

bilateral subtraction; cancer; deep learning; enhancement filter; image segmentation; image security; mammography

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