Merge pull request #19 from DerikVo/readme

added a streamlit section

Author: DerikVo

README.md

@@ -8,6 +8,7 @@ Table of contents
 |[Problem_Statement](#Problem_Statement)|
 |[EDA](#EDA)|
 |[Modeling_and_Evaluations](#Modeling_and_Evaluations)|
+|[Streamlit](#Streamlit)|
 |[Conclusion](#Conclusion)|
 |[Recommendations](#Recommendations)|
 |[Limitations](#Limitations)|
@@ -74,6 +75,12 @@ Our models were evaluated in the [Model Evaluation notebook](./Notebooks/04_Mode
 
 When looking at additional metrics we have more insight on the different metrics on individual classifications. In this case we only want to focus only on the no tumor class in which case we want to limit our false negative, so the metric we will focus on is Precision. In this case our Neural network with no regularization does the best across all metrics. The only mis-classification for the no tumor class is meningioma in which case we see 64 false negatives.
 
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+## Streamlit
+
+A [streamlit app](./Streamlit/app.py) was developed to allow radiologist to upload an image to get some predictions and the probability of an image belonging to a class. A future feature is to have user input the update the class so the weights can be updated. There was also a planned feature to allow developers to upload a model stored as an h5 model and have that be evaluated. However when uploading a model it does not get stored as an h5 file. Currently the feature requires developers to assign a path to the stored model. As of now it only displays the metric scores and saved confusion matrix (image) of a model. In the future the confusion matrix will also be dynamic like the metric score, and incorporate the ability to upload a model so developers and IT can update the model being used.
+
+
 ## Conclusion
 
 Overall our best model has similar accuracy (87%) to manual classification from the radiologist found in the study by [Munir, S. Khan, Hanif, and M. Khan (2021)](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7794124/) so our model could greatly help reduce the workload of radiologist. We primarily care about the false negative rate of our no tumor class and found the model to often predict meningioma incorrectly. [According to Zhang et al. (2022)](https://www.frontiersin.org/articles/10.3389/fonc.2022.886968/full) meningiomas are often benign and not as a severe as a risk as the other two classes of tumors. However, it still the risk of the missing a cancerous meningoma still remains, so that risk is still present.