Detect the Activity of Benign and Malignant Breast Cancer
DOI:
https://doi.org/10.59890/ijist.v2i5.1870Keywords:
Breast Cancer Detection, Convolutional Neural Network, Transfer Learning, Data Augmentation, VGG-16 ModelAbstract
Breast cancer detection is an important stage for early cancer diagnosis. In this study, a Convolutional Neural Network (CNN) algorithm is used to detect breast cancer. The dataset used consists of MRI scan images of benign and malignant breast cancer, which are processed through breast image cropping and data augmentation. The model was trained using CNN architecture with transfer learning method of VGG-16 model. The results of the model training showed good performance with an accuracy of 62%. These findings show the potential of using CNN and transfer learning in improving early detection of breast cancer.
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