Hi,
In response to your initial inquiry, I would like to highlight several existing deep learning architectures that can be effectively utilized:
- Convolutional Neural Networks (CNNs): These are particularly well-suited for image data, as they efficiently manage multiple channels, such as RGB.
- 3D Convolutional Neural Networks (3D CNNs): These networks are adept at processing volumetric data through the use of 3D convolutions, capturing depth, height, and width.
- UNet/SegNet: These architectures are beneficial for segmentation tasks, especially when dealing with multilayered spatial data.
- Recurrent Neural Networks (RNNs) and Long Short-Term Memory Networks (LSTMs): These models are designed to capture spatial and sequential dependencies within temporal data.
- YOLO (You Only Look Once): This architecture is known for its fast real-time object detection capabilities, simultaneously predicting class labels and bounding boxes.
To develop a customized deep learning architecture capable of extracting spatial data from input layers, you may consider employing a 3D CNN to extract features across the x, y, and z axes. Additionally, transfer learning can be leveraged by utilizing a pretrained model, replacing the final layers to suit your specific task, and subsequently fine-tuning the model. Pretrained 3D UNet or 3D ResNet models could serve as valuable starting points for this endeavour.
You can check documentation for pretrained model as well -
