Modified VGG19 transfer learning model for breast cancer classification

Breast cancer (BC) seems to have become a sign of great concern in everyday life. There have been a lot of research and methods already designed in the last few years but continue to be prone worldwide. To address this issue, a modified version of the visual geometric group-19 (VGG19) model, namely BCNet21 has been proposed here to classify the malignant class from breast mammogram images collected from the MIAS dataset. Furthermore, the performance of our proposed BCNet21 model has been compared with the two most common predefined VGG16 and VGG19 models using the performance metrics and Cohen-Kappa test (k). The result shows that the proposed BCNet21 model outperforms with a higher accuracy of 98.96 % and a kappa score of 86%, compared to the VGG16 and VGG19 models. This concludes that the BCNet21 model is much closer to the near-perfect agreement between actual and predicted breast cancer instances.