Prediction of Breast Cancer images Classification Using Bidirectional Long Short Term Memory and Two-Dimensional Convolutional Neural network - Adeniji, oluwashola David (Discoveries in Agriculture and Food Sciences, 2021)
Breast cancer is most prevalent among women around the world and Nigeria is no exception in this menace. The increased in survival rate is due to the dramatic advancement in the screening methods, early diagnosis, and discovery in cancer treatments. There is an improvement in different strategies of breast cancer classification. A model for training deep neural networks for classiﬁcation of breast cancer in histopathological images was developed in this study. However, this images are aﬀected by data unbalance with the support of active learning. The output of the neural network on unlabeled samples was used to calculate weighted information entropy. It is utilized as uncertainty score for automatic selecting both samples with high and low conﬁdence. A threshold that decays over iteration number is used to decide which high conﬁdence samples should be concatenated with manually labeled samples and then used inﬁne-tuning of convolutional neural network. The neural network was optionally trained using weighted cross-entropy loss to better cope with bias towards the majority class. The developed model was compared with the existing model. The accuracy level of 98.3% was achieved for the developed model while the existing model 93.97%. The accuracy gain of 4.33%. was achieved as performance in the prediction of breast cancer .