Teaching Material for a HackClub workshop

Krish

Objective: Make an AI figure out whether a tumor is malignant or benign. You can learn more about this here. Some more in depth resources to understand this if you are interested is:

  1. https://www.nhs.uk/conditions/malignant-brain-tumour/
  2. https://jamanetwork.com/journals/jamaoncology/fullarticle/2768634
  3. https://www.nhs.uk/conditions/benign-brain-tumour/
  4. https://www.nhsinform.scot/illnesses-and-conditions/cancer/cancer-types-in-adults/malignant-brain-tumour-cancerous
  5. https://www.cancer.gov/about-cancer/understanding/what-is-cancer

What is this GitHub repository for?

  • Good for teaching you the basics of using TensorFlow and neural networks
  • Not ideal for using the code as is – must be modified to use in actual application

Why this is important?

  • About 1 in 8 women (US) will be diagnosed with breast cancer – https://www.breastcancer.org/symptoms/understand_bc/statistics
  • Correct cancer diagnosis is important for an “appropriate and effective treatment”, as was also claimed by the WHO
  • Some kinds of cancer are misdiagnosed at rates as high as 61 percent, according to the American Cancer Society (https://www.cancer.org/)
  • Estimates demonstrate that ~ 20% of all cases of cancer are misdiagnosed (so ~ 80% will get the right treatment and diagnosis on first visit). Some will be sent back home with wrong treatment or diagnosis
  • Thus, women or men diagnosed with breast cancer often consult a second oncologist to ensure treatment/diagnosis is correct (costs money, long wait). Get second consultation from ML (can be trained as well as doctor, is faster than a doctor’s diagnosis, not expensive)

Key Stuff

  • Malignant (Tumor that must be removed)
  • Benign (Tumor that is not cancerous)
  • Supervised ML – We know whether tumor is malignant or benign (labeled data)
  • Cleaned data – Clean it of not needed useless/relevant info for this task of tumor classification
  • 80% training data, 20% test data

Install dependencies:

pip3 install pandas sklearn tensorflow keras 
  1. Diagnosis
  • 1 – Malignant
  • 2 – Benign
  1. Numerical values relating to tumors that may or may not be cancerous – measurements describe properties of the cell’s nucleus (eg, perimetr, texture, area, size )

References for datasets, TensorFlow docs and papers on cancer survival to shed light into how important proper diagnosis is

Demonstration:

Watch it train ?

asciicast


**
DISCLAIMER: I am not a medical professional**

GitHub

View Github