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Medical Image Forgery Detection

Dilipkumar, Rithin krishna (2022) Medical Image Forgery Detection. Masters thesis, Dublin, National College of Ireland.

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Currently, the majority of businesses and organizations rely on internet services because of the improvements in technology and how simple they are to acquire and use. In the medical sector, digital photographs have also gained more significance than before. X-rays, CT scans, and MRI scans are just a few of the medical information that are now preserved and shared digitally. It has been demonstrated that both patients and medical professionals can more easily access and save digital photographs, making them more useful. Cybercriminals may have simple access to private patient information thanks to the ease with which medical photographs are available online, which might have extremely negative consequences. This study recommends using deep learning techniques to develop the Random Forest and ResNet-50 models, as well as algorithms like Watershed, to accurately detect manipulation in medical photographs. The major objective of this study is to correctly complete the research by achieving maximum accuracy utilizing faster deep learning algorithms and a big, current data collection.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Computer software > Computer Security
T Technology > T Technology (General) > Information Technology > Computer software > Computer Security
Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions: School of Computing > Master of Science in Cyber Security
Depositing User: Tamara Malone
Date Deposited: 19 Dec 2022 15:32
Last Modified: 07 Mar 2023 17:27

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