Leveraging Transfer Learning for High-Accuracy Breast CancerClassification f rom Histopathological Images

Early detection of breast cancer remains an important global health concern. Inthis paper, we present a novel method for classifying breast cancer usinghistopathological images from the BreakHis dataset at 400X resolution. Weextract high-level features capturing malignancy patterns using VGG19 andDenseNet201. For final classification, these features are concatenated and fed intoan Artificial Neural Network (ANN), which achieves an impressive accuracy of99%. The high accuracy of our methodology demonstrates its potential as aneffective diagnostic tool in the digital pathology era.

کلیدواژه های Leveraging Transfer Learning for High-Accuracy Breast CancerClassification f rom Histopathological Images:

Breast cancer ، Breakhsi Dataset ، Transfer Learning ، VGG19 ، DenseNet20