Deep ensemble transfer learning for detecting breast cancer in histopathological images
Deep ensemble transfer learning for detecting breast cancer in histopathological images
Indo Intan,B. Nelwan,3 Authors,Bens Pardamean
TLDR
A deep learning solution to solve the problem of detecting breast cancer malignancy using deep ensemble convolutional neural network models constructed from single and smaller ensemble models.
Abstract
. Among other cancer type, breast cancer is the leading cause of death worldwide. The traditional approach in detecting breast cancer malignancy relied on rigorous analysis, making the whole process prone to diagnostic error. This study proposes a deep learning solution to solve the problem using deep ensemble convolutional neural network (CNN) models constructed from single and smaller ensemble models. The utilized single models are ResNet50V2, InceptionResNet50V2, DenseNet201, EfficientNetB4, EfficientNetV2S, and Xception. Smaller ensemble combinations are also made from the single models. The deep ensemble models composed of ResNet50V2-EfficientNetV2S-DenseNet201, EfficientNetB4-EfficientNetV2S-Xception, EfficientNetB4-EfficientNetV2S, DenseNet201-EfficientNetB4, and ResNet50V2-DenseNet201.