Image classification using a hybrid BiLSTM-CNN for breast cancer

This research aims to combine BiLSTM and CNN to provide a new model for applications in image classification. A BiLSTM network is a long-term recurrent neural network (RNN) that can recall dependencies. The main potential of BiLSTM, when used in a multi-layer configuration, is to enhance a CNN’s feature extraction capabilities. CNN is able to glean useful information from models that BiLSTM can selectively remember for a long time. The model proposed has performance superior to the standard CNN classifier in image classification. This is due to the data passing through two stages and several layers, the first through BiLSTM and the second through the CNN stage, each of which contains several layers and filters, resulting in high accuracy compared to previous studies. This is because convolutional and recurrent neural networks work together to form the suggested model. This model can be trusted by working great with neural networks for all types of classification tasks. Researchers validated these results by testing our model on a real dataset. The proposed model’s training accuracy is (0.9982), and its testing accuracy is (0.9633), indicating its success and suitability compared to other models.