We design a lightweight CNN architecture for the chest x-ray classi-32 fication task by introducing ExLNet which uses a novel DCISE blocks to reduce the33 computational burden. We show the effectiveness of the proposed architecture through34 various experiments performed on publicly available datasets. The proposed architec-35 ture shows consistent performance in binary as well as multi-class classification tasks36 and outperforms other lightweight CNN architectures. Due to a significant reduction37 in the computational requirements, our method can be useful for resource-constrained clinical environments.

Journal link: https://aapm.onlinelibrary.wiley.com/doi/10.1002/mp.16722