Akgül, Ismail; Kaya, Volkan; Karavaş, Erdal; Aydin, Sonay; Baran, Ahmet

The interpretation of breast magnetic resonance imaging (MRI) in the healthcare field depends on the good knowledge and experience of radiologists. Recent developments in artificial intelligence (AI) have shown advances in the field of radiology. However, the desired levels have not been reached in the field of radiology yet. In this study, a novel model structure is proposed to characterize the diagnostic performance of AI technology for individual breast dynamic contrast material–enhanced (DCE) MRI sequences. In the proposed model structure, Inception-v3, EfficientNet-B3, and DenseNet-201 models were used as hybrids together with the Yolo-v3 algorithm to detect breast and cancer regions. In the proposed model, DCE-MRI sequences (T2, ADC, Diffusion, Non-Contrast Fat Non-Suppressed T1, Non-Contrast Fat Suppressed T1, Contrast Fat Suppressed T1, and Subtraction T1) were evaluated separately and validation was made, thus providing a unique perspective. According to the validation results, the model structure with the best performance was determined as Yolo-v3 + DenseNet-201. With this model structure, 92.41% accuracy, 0.5936 loss, 92.44% sensitivity, and 92.44% specificity rates were obtained. In addition, it was determined that the results obtained without using contrast material in the best model were 91.53% accuracy, 0.9646 loss, 92.19% sensitivity, and 92.19% specificity. Therefore, it is predicted that the need for contrast material use can be reduced with the help of this model structure.

Ilgin, F.Y.

In this study, an energy-based spectrum sensing method combined with copula theory is proposed for cognitive radio systems. In the proposed spectrum sensing model, cognitive radio users first make their own local spectrum decision with energy-based spectrum sensing. Then, they forward their decision to the fusion center. In the fusion center, this decision is compared with the threshold value determined by copula theory and global spectrum decision is made. The test statistic at the fusion center were obtained with the Neyman Pearson approach. Thus, the fusion rule was created for the fusion center and necessary simulation studies were performed. According to the results of the simulation studies, the proposed detection method showed better results than the traditional energy based detection method.