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This is an official telegram channel of the Center for Interdisciplinary Applied Research of Central Asian University (CAU), Tashkent, Uzbekistan. CAU-affiliated students and colleagues may join the channel to keep abreast of research-related news at CAU.
This is an official telegram channel of the Center for Interdisciplinary Applied Research of Central Asian University (CAU), Tashkent, Uzbekistan. CAU-affiliated students and colleagues may join the channel to keep abreast of research-related news at CAU.
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20TH SCF INTERNATIONAL CONFERENCE “ECONOMIC, SOCIAL, AND MANAGERIAL IMPLICATIONS OF INFORMATION & COMMUNICATION TECHNOLOGIES” 26-27 APRIL 2024 / ISTANBUL– TURKIYE 20th SCF International Conference on “Economic, Social, and Managerial Implications of Information & Communication Technologies” organized by Bandirma Onyedi Eylul University & SCF Society will be held in Ramada by Wyndham-Istanbul Taksim in Turkiye, during the period…
Objectives Early skin cancer diagnosis can save lives; however, traditional methods rely on expert knowledge and can be time-consuming. This calls for automated systems using machine learning and deep learning. However, existing datasets often focus on flat skin surfaces, neglecting more complex cases on organs or with nearby lesions. Methods This work addresses this gap by proposing a skin cancer diagnosis methodology using a dataset named ASAN that covers diverse skin cancer cases but suffers from noisy features. To overcome the noisy feature problem, a segmentation dataset named SASAN is introduced, focusing on Region of Interest (ROI) extraction-based classification. This allows models to concentrate on critical areas within the images while ignoring learning the noisy features. Results Various deep learning segmentation models such as UNet, LinkNet, PSPNet, and FPN were trained on the SASAN dataset to perform segmentation-based ROI extraction. Classification was then performed using the dataset with and…