Breast Cancer Detection fromHistopathological Images using DeepLearning and Transfer Learning

Breast Cancer is the most common cancer in women and it's harming women's mental and physical health. Due to complexities present in Breast Cancer images, image processing technique is required in the detection of cancer. Early detection of Breast cancer required new deep learning and transfer learning techniques. In this paper, histopathological images are used as a dataset from Kaggle. Images are processed using histogram normalization techniques. This research project implements the Convolutional Neural Network(CNN) model based on deep learning and DenseNet-121 based on transfer-learning. Transfer learning uses the Imagenet pre-trained model for training. Hyper-parameter tuning is done for increasing accuracy and precision value. Research achieved 90.9 % test accuracy using the CNN model and 88.03 % accuracy by the transfer learning model.