این سایت در حال حاضر پشتیبانی نمی شود و امکان دارد داده های نشریات بروز نباشند
International Journal of Nonlinear Analysis and Applications، جلد ۱۲، شماره Special Issue، صفحات ۱۲۶۹-۱۲۸۲
عنوان فارسی
چکیده فارسی مقاله
کلیدواژه‌های فارسی مقاله
عنوان انگلیسی A new parallel deep learning algorithm for breast cancer classification
چکیده انگلیسی مقاله Now diagnostic methods with the help of machine learning have been able to help doctors in this field. One of the most important of these methods is deep learning, which has gotten good answers in images containing cancer. Increasing the accuracy of deep neural network classifiers can increase the diagnosis of breast cancer. In this paper, we have tried to achieve higher accuracy than non-parallel models with the help of a parallel model of a deep neural network. The proposed method is a parallel hybrid method combining AlexNet and VGGNet networks applied in parallel to mammographic images. The database used in this article is INBreast. The results obtained from this method show a 4% increase compared to some other classification models so that in the type of density 1, it has achieved about 99.7%. In the case of other densities, an accuracy of nearly 99% has been obtained.
کلیدواژه‌های انگلیسی مقاله Medical Image, Magnetic Resonance Imaging, parallel convolutional neural network
نویسندگان مقاله Ahmad Kazemi |
Department of Computer Engineering, Sanandaj Branch, Islamic Azad University, Sanandaj, Iran

Mohammad Ebrahim Shiri |
Computer Science Department, Amirkabir University of Technology, Tehran, Iran

Amiri Sheikhahmadi |
Department of Computer Engineering, Sanandaj Branch, Islamic Azad University, Sanandaj, Iran

Mohamad Khodamoradi |
Department of Mathematics, Izeh Branch,Islamic Azad University, Izeh,Iran

نشانی اینترنتی https://ijnaa.semnan.ac.ir/article_5642_bfc10aec38def2e3cf02a518c3746c66.pdf
فایل مقاله فایلی برای مقاله ذخیره نشده است
کد مقاله (doi)
زبان مقاله منتشر شده en
موضوعات مقاله منتشر شده
نوع مقاله منتشر شده
برگشت به: صفحه اول پایگاه   |   نسخه مرتبط   |   نشریه مرتبط   |   فهرست نشریات