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Full-Text Articles in Medicine and Health Sciences
An Xai Approach For Covid-19 Detection Using Transfer Learning With X-Ray Images, Salih Sarp, Ferhat Ozgur Catak, Murat Kuzlu, Umit Cali, Huseyin Kusetogullari, Yanxiao Zhao, Gungor Ates, Ozgur Guler
An Xai Approach For Covid-19 Detection Using Transfer Learning With X-Ray Images, Salih Sarp, Ferhat Ozgur Catak, Murat Kuzlu, Umit Cali, Huseyin Kusetogullari, Yanxiao Zhao, Gungor Ates, Ozgur Guler
Engineering Technology Faculty Publications
The coronavirus disease (COVID-19) has continued to cause severe challenges during this unprecedented time, affecting every part of daily life in terms of health, economics, and social development. There is an increasing demand for chest X-ray (CXR) scans, as pneumonia is the primary and vital complication of COVID-19. CXR is widely used as a screening tool for lung-related diseases due to its simple and relatively inexpensive application. However, these scans require expert radiologists to interpret the results for clinical decisions, i.e., diagnosis, treatment, and prognosis. The digitalization of various sectors, including healthcare, has accelerated during the pandemic, with the use …
Simultaneous Wound Border Segmentation And Tissue Classification Using A Conditional Generative Adversarial Network, Salih Sarp, Murat Kuzlu, Manisa Pipattanasomporn, Ozgur Guler
Simultaneous Wound Border Segmentation And Tissue Classification Using A Conditional Generative Adversarial Network, Salih Sarp, Murat Kuzlu, Manisa Pipattanasomporn, Ozgur Guler
Engineering Technology Faculty Publications
Generative adversarial network (GAN) applications on medical image synthesis have the potential to assist caregivers in deciding a proper chronic wound treatment plan by understanding the border segmentation and the wound tissue classification visually. This study proposes a hybrid wound border segmentation and tissue classification method utilising conditional GAN, which can mimic real data without expert knowledge. We trained the network on chronic wound datasets with different sizes. The performance of the GAN algorithm is evaluated through the mean squared error, Dice coefficient metrics and visual inspection of generated images. This study also analyses the optimum number of training images …