Deep learning based model for Cyro ET Sub-tomogram-Detection
High degree of structural complexity and practical imaging constraints make retrieval of
macromolecular structures from cryo-ET is very challenging. For image classification of
large-scale systematic macro-molecular structure from cryo-ET data.
For image classification of large-scale systematic macro-molecular structure from cryo-ET data, a
deep learning-based image classification approach has been employed to improve the
accuracy for a small range of SNR values where the present models have fallen short.
Here, a novel SEC3 model for macro-molecule separation has been used.
The model comprises 3D convolutional blocks and 3D squeeze and excitation blocks. The
model is trained on subtomogram datasets divided into 3 SNR values. Each SNR value
namely- 0.03, 0.05, and infinity is further divided into 10 different classes based on their
shape. The model understands by learning the valuable spatial information and
spectral information available in the macromolecular