Update README.md

README.md

@@ -36,18 +36,3 @@ The ViT had the best regression results, with an MSE of 0.5135. Code developed i
 We were fine-tuned the Vision Transformer (ViT) from HuggingFaces on our training set for 300 epochs using an AdamW optimizer with a learning rate of 0.00001 and a batch size of 10. 
 Early stopping was applied to the model to prevent over-fitting.
 For ViT, training stopped after 70 epochs. The only difference between the preprocessed data in the ViT and the other models was that it normalized to a tensor image with mean and standard deviation during data augmentation.
-
-## Citation
-If you use this work in your research, please cite it as follows:
-
-```bibtex
-@misc{lara2023covid,
-  title={Diagnosing COVID-19 Severity from Chest X-Ray Images Using ViT and CNN Architectures},
-  author={Luis Lara and Lucia Eve Berger and Rajesh Kumar Raju and Shawn Whitfield},
-  year={2023},
-  archivePrefix={arXiv},
-  eprint={2502.16622},
-  primaryClass={cs.CV},
-  url={https://arxiv.org/abs/2502.16622}
-}
-```