added more to the readme. fleshed out problem statement and introduction

Author: DerikVo

Notebooks/01_EDA.ipynb

@@ -638,7 +638,7 @@
     "\n",
     "Overall, our 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 can see some distinct features within those classes. This indicates that there are features that our model can learn to distinguish images into classes.\n",
     "\n",
-    "We will know move onto building [A baseline model](../Notebooks/01_EDA.ipynb) to compare our results."
+    "We will know move onto building a [baseline model](../Notebooks/01_EDA.ipynb) to compare our results."
    ]
   }
  ],

README.md

@@ -5,9 +5,27 @@ This project was a part of my capstone project for General assembly. The goal wa
 
 According to [Munir, S. Khan, Hanif, and M. Khan (2021)](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7794124/) found that Giloma tumors are the most common type of tumor. They found that identification of these tumors has an accuracy of 87% (N = 154), using histopathology also known as "the study of diseased cells and tissues using a microscope" [cancer.gov](https://www.cancer.gov/publications/dictionaries/cancer-terms/def/histopathology). As a note, the radiologists reviewing the images had a minimum of five years of post fellowship experience. [Morgan (2022)](https://www.cancer.gov/rare-brain-spine-tumor/blog/2022/neuroradiology) explains the first MRI image is typically a baseline and subsequent scans are used to determine if there is a change. Morgan argues that determining the results of an MRI scan can often be an inefficient process due to spending "15 mintues or more arguing about weather something had change on the MRI". [Zacharaki et al. (2009)](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2863141/) argues the difficulty of an MRI diagnoses is due to tissue often being heterogeneous and often difficult to distinguish with the human eye.
 
+[According to AppliedRadiology (2023)](https://appliedradiology.com/articles/the-radiologist-s-gerbil-wheel-interpreting-images-every-3-4-seconds-eight-hours-a-day-at-mayo-clinic) found that radiologist have about three to four seconds to review an image to meet workplace demands. Because of this work demand there is a need to support radiologist in interpreting an MRI scan.
+
+In addition to the radiologist there are a few more stakeholders we need to be aware of.
+Stake holders:
+
+|Stakeholder|reason|
+|-----------|-------|
+|Data Scientist|Develop and train the model|
+|Medical Staff|provide domain expertise, validate output, and interpret results. Works with patients|
+|Data Team|Collects, cleans, and provides the right data|
+|Legal Team|Ensure we are following legal procedure. Ensure we are maintaining confidentiality|
+|Regulatory bodies|Ensure we are in compliance with regulation requirements|
+|Administrators|Decide how and if we implement the model|
+|IT professionals|Ensure the model is functioning smoothly, and first point of contact for trouble shooting|
+|Patients|Consent to their data being used. Diagnosis impacts their leaves, so being mis-diagnosed or un-diagnosed has a significant impact|
+|MRI manufacturers|May design their MRI based on the needs of our model, or provide insights to improve our model|
+|Researchers|Can be a point of collaboration to improve model performance or provide expertise|
 
 ## Problem Statement:
-Because of the difficulty of manual classification of a brain tumor building a classification model would greatly reduce the time spent in the MRI room and help guide the MRI process.
+Because of the difficulty of manual classification of a brain tumor and the time constraints put on radiologist, building a classification model would greatly reduce the workload of a radiologist. This model will help guide the MRI process by classifying the images so the radiologist will have a reference point when evaluating an MRI scan. This model should only be used as an assistant and not for diagnosis.
+For the purposes of this model, we want to limit our false negatives. We want to avoid the model predicting no tumor when in reality there is a tumor. A false positive,predicting there is a tumor when there is no tumor, would simply require a follow up from a radiologist or specialized professional to confirm a diagnosis. In this case the model cares more about the precision and accuracy score of the model.
 
 ## EDA