A new parallel deep learning algorithm for breast cancer classification | ||
| International Journal of Nonlinear Analysis and Applications | ||
| دوره 12، Special Issue، اسفند 2021، صفحه 1269-1282 اصل مقاله (680.42 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2021.24247.2702 | ||
| نویسندگان | ||
| Ahmad Kazemi* 1؛ Mohammad Ebrahim Shiri2؛ Amiri Sheikhahmadi1؛ Mohamad Khodamoradi3 | ||
| 1Department of Computer Engineering, Sanandaj Branch, Islamic Azad University, Sanandaj, Iran | ||
| 2Computer Science Department, Amirkabir University of Technology, Tehran, Iran | ||
| 3Department of Mathematics, Izeh Branch,Islamic Azad University, Izeh,Iran | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| Medical Image؛ Magnetic Resonance Imaging؛ parallel convolutional neural network | ||
| مراجع | ||
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