Merge pull request #10 from DerikVo/readme

Added to the EDA section of the read me

Author: DerikVo

Notebooks/01_EDA.ipynb

@@ -473,7 +473,7 @@
    "id": "aab3465a-35e4-43ed-9935-ef321e77f4c5",
    "metadata": {},
    "source": [
-    "As we can see there is a clear difference between each class of images here. The no tumor class looks most like a clear image while the other types of tumors begin to lose details. Especially the Giloma Tumor and Meningioma Tumor, which given their similar names makes sense. This difference between these two tumor types and no tumor could be due to a suspicion of a tumor can warrant taking scans at different angles. This would explain why there are some differences between the classes. \n"
+    "As we can see there is a clear difference between each class of images here. The no tumor class looks most like a clear image while the other types of tumors begin to lose details. Especially the Giloma Tumor and Meningioma Tumor, which begins to look like a blurry dot. This difference between these two tumor types and no tumor could be due to a suspicion of a tumor can warrant taking scans at different angles. This would explain why there are some differences between the classes. When reflecting on our research [Morgan (2022)](https://www.cancer.gov/rare-brain-spine-tumor/blog/2022/neuroradiology) suggest an initial MRI scan is used to determine how to proceed, so if the brain is healthy then there is no need for additional imaging. Therefore that explains why the no tumor class is generally more detailed than other images.\n"
    ]
   },
   {

README.md

@@ -29,6 +29,18 @@ For the purposes of this model, we want to limit our false negatives. We want to
 
 ## EDA
 
+For our [EDA process](../Notebooks/01_EDA.ipynb) we examined our two datasets which contained the classes 'glioma', 'meningioma', 'notumor', 'pituitary' which were separated into their own folders. The file count of the images were as follows:
+
+|Class|Training count| Testing count|
+|------|------|------|
+|No Tumor|1,595|405|
+|Meningioma Tumor|1,339|306|
+|Glioma Tumor|1,321|300|
+|Pituitary Tumor|1,457|300|
+
+
+For our EDA process we took the average pixel value of each class.
+Overall, the classes are balanced between the classes 'glioma', 'meningioma', 'notumor', and 'pituitary' in both our training and testing datasets. Additionally when looking at the average pixel value and the contrast between those average we saw some distinct features within those classes. This indicates that there are features that our model can learn to distinguish images into classes. One interesting observation we made was brain with no tumors generally had the most details. When reflecting on our research, [Morgan (2022)](https://www.cancer.gov/rare-brain-spine-tumor/blog/2022/neuroradiology) suggested the first MRI scan is used as a baseline to determine what areas of the brain to scan. This suggest a brain with no tumor will generally have the same angle across patients which was shown in our analysis.
 
 ## Modeling