Hello Mahmoud,
To achieve image classification with a CNN and concurrently classify signals with an LSTM, you can employ deep learning networks. Here's a structured approach:
Construct the CNN for Image Classification, build a sequence of convolutional layers with activation functions like ReLU to process images and extract their features.
Develop the LSTM Network for Signal Classification, initiate with an input layer tailored for sequential data such as signals. Insert LSTM layers to handle the time-series data and identify time-related patterns. Post-LSTM, add dense layers to make sense of the extracted sequence features.
Merge the CNN and LSTM for Concurrent Processing, Forge two distinct input pathways, one for the CNN (handling images) and another for the LSTM (processing signals). Each pathway should analyse its input and extract relevant features. Fuse the outputs from both pathways to add the feature sets. If necessary, introduce more dense layers to effectively blend the features for the classification task. To add 2 layers together you’ll need to use ‘additionLayer’. Please go through this example to know how to do this,
After you can follow the normal procedure of Neural Networks training like defining optimizers and all. For a practical example of integrating CNN with LSTM, you can refer to this resource:
Additionally, to deepen your understanding of the layers and their functions,
Hope this helps.
