We targeted to design a light weight and robust architecture for optical coherence tomography classification while maintain state of the art accuracy. With custom designed blocks and deep research in light weight computations we were able to achieve the state of the art accuracy with only three million model parameters instead of fifty million model parameters. This reflects how designing light weight models can be useful as much as deeper models. The research resulted in a publication.
Link to publication is here