--- a/README.md
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@@ -1,41 +1,37 @@
-# ECGTransForm: Empowering Adaptive ECG Arrhythmia Classification Framework with Bidirectional Transformer [[Paper](https://www.sciencedirect.com/science/article/pii/S1746809423011473)] [[Cite](#citation)]
-#### *by: Hany El-Ghaish, Emadeldeen Eldele*
-#### This work is accepted for publication in the Biomedical Signal Processing and Control.
-
-## About
-![ECGTransForm Architecture](misc/ecgTransform.png)
-Our proposed model, ECGTransForm, is a deep learning framework for ECG arrhythmia classification, featuring a novel Bidirectional Transformer mechanism and Multi-scale Convolutions for effective spatial and temporal feature extraction. The framework also includes a Context-Aware Loss to handle the class imbalance in ECG data, demonstrating superior performance in arrhythmia diagnosis.
-
-
-## Datasets
-We used two public datasets in this study (Download our preprocessed version of the datasets from [Google Drive](https://drive.google.com/file/d/1eZ7NS7mED2ZCU2YDbeWMmFAc2TsPsX0E/view?usp=sharing)):
-- [MIT-BIH](https://www.physionet.org/content/mitdb/1.0.0/)
-- [PTB](https://physionet.org/content/ptbdb/1.0.0/)
-
-## Configurations
-There are two configuration files: 
-- one for dataset configuration `configs/data_configs.py`
-- one for training configuration `configs/hparams.py`
-
-
-## Results
-<p align="center">
-<img src="misc/ecgTransform_res.png" width="800" class="center">
-</p>
-
-## Citation:
-If you found this work useful for you, please consider citing it.
-```
-@ARTICLE{ecgTransForm,
-    title = {ECGTransForm: Empowering adaptive ECG arrhythmia classification framework with bidirectional transformer},
-    journal = {Biomedical Signal Processing and Control},
-    volume = {89},
-    pages = {105714},
-    year = {2024},
-    issn = {1746-8094},
-    doi = {https://doi.org/10.1016/j.bspc.2023.105714}, 
-    url = {https://www.sciencedirect.com/science/article/pii/S1746809423011473},
-    author = {Hany El-Ghaish and Emadeldeen Eldele},
-}
-```
-
+# ECGTransForm: Empowering Adaptive ECG Arrhythmia Classification Framework with Bidirectional Transformer [[Paper](https://www.sciencedirect.com/science/article/pii/S1746809423011473)] [[Cite](#citation)]
+#### *by: Hany El-Ghaish, Emadeldeen Eldele*
+#### This work is accepted for publication in the Biomedical Signal Processing and Control.
+
+## About
+Our proposed model, ECGTransForm, is a deep learning framework for ECG arrhythmia classification, featuring a novel Bidirectional Transformer mechanism and Multi-scale Convolutions for effective spatial and temporal feature extraction. The framework also includes a Context-Aware Loss to handle the class imbalance in ECG data, demonstrating superior performance in arrhythmia diagnosis.
+
+
+## Datasets
+We used two public datasets in this study (Download our preprocessed version of the datasets from [Google Drive](https://drive.google.com/file/d/1eZ7NS7mED2ZCU2YDbeWMmFAc2TsPsX0E/view?usp=sharing)):
+- [MIT-BIH](https://www.physionet.org/content/mitdb/1.0.0/)
+- [PTB](https://physionet.org/content/ptbdb/1.0.0/)
+
+## Configurations
+There are two configuration files: 
+- one for dataset configuration `configs/data_configs.py`
+- one for training configuration `configs/hparams.py`
+
+
+
+
+## Citation:
+If you found this work useful for you, please consider citing it.
+```
+@ARTICLE{ecgTransForm,
+    title = {ECGTransForm: Empowering adaptive ECG arrhythmia classification framework with bidirectional transformer},
+    journal = {Biomedical Signal Processing and Control},
+    volume = {89},
+    pages = {105714},
+    year = {2024},
+    issn = {1746-8094},
+    doi = {https://doi.org/10.1016/j.bspc.2023.105714}, 
+    url = {https://www.sciencedirect.com/science/article/pii/S1746809423011473},
+    author = {Hany El-Ghaish and Emadeldeen Eldele},
+}
+```
+