نوع مقاله : مقاله پژوهشی
نویسنده
گروه کامپیوتر ، دانشکده فنی و مهندسی، دانشگاه پیام نور، تهران، ایران
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسنده [English]
Early and accurate diagnosis of breast cancer based on mammography images can greatly help doctors in timely treatment of the disease. On the other hand, most of the deep networks used in breast cancer (BC) detection systems are not suitable for real-time applications due to the use of fully connected layers, time-consuming training method and high calculation volume. Therefore, in this article, a new hybrid method based on deep learning and crowd intelligence called EZFNet-ELM-COA is proposed for real-time detection of breast cancer (BC). In the proposed EZFNet network, in the convolutional layers of the ZFNet network, large size filters have been replaced by two series of smaller size filters, and then the outputs of the filters have been joined together. The use of this multi-scale technique makes the network extract more effective features. Also, in order to increase the speed of training and significantly reduce the volume of calculations, fully connected (FC) layers of the pre-trained EZFNet network were replaced by ELM. At the end, the generalization capability of ELM has been improved by updating the weights and biases of the ELM layer by the Quati Optimization Algorithm (COA). In order to check the performance of the system, six evaluation criteria have been used, and our proposed model has the following criteria: accuracy, precision, sensitivity, recognizability, F1-score and MCC values respectively: 97.97, 95.70, and 15. 97, 98.28, 96.45 and 93.98 percent have been obtained. The obtained results show the increase of accuracy, improvement of efficiency, significant reduction of calculation time and high performance level of the proposed system compared to the existing methods.
کلیدواژهها [English]
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