Breast Cancer Prediction and Control Using BiLSTM and Two-Dimensional Convolutional Neural Network

Breast cancer has a devastating effect on women. Different strategies of breast classification exist with minimal work done the prediction occurrence disease in potential carriers. In this study, predictive system been developed using bidirectional long short-term memory (BiLSTM) for feature extraction and learning while two-dimensional convolutional neural network (CNN) was used classification. Histopathological images were prediction. Python as programming language implementing system. The model tested datasets from Cancer Imaging Archive (TCIA) repository. An accuracy level 98.8% (higher than most recent existing model) achieved future based tests dataset. application live data women can help control amongst