LifeSavr

Inspiration

Seeing patients who have Melanoma struggling with their outwards perception with others and that I wanted to put an end to the cancer getting worse by recognizing the severity of the cancer first-hand instead of waiting till the cancer is fully developed on the patient.

What it does

It is an IOS app that uses Machine Learning along with a device's camera to predict the severity of Melanoma on a patient's skin.

How I built it

First of all, I used Xcode to build this IOS app. Then, I developed a CoreMl model which predicts the severity of the cancer by pointing to it using a phone's camera. (The picture of the sample of Melanoma could be a picture of it on a device or a real-life picture of it on a person's skin)

Challenges I ran into

Making the camera even pop up when I load the app. Correctly diagnosing the severity of the Melanoma Cancer.

Accomplishments that I'm proud of

Seeing my app diagnose and recognize Melanoma on a person accurately and precisely.

What I learned

I learned that Machine Learning is extremely useful in diagnosing cancer because it can learn from the pictures that were used to train it and accurately diagnose the severity of the cancer on the person or patient.

What's next for LifeSavr

Making it an open-source project where anyone can collaborate and improve on the app. Trying my app on real-life Melanoma patients and feeling the satisfaction of creating a medical breakthrough in recognizing and diagnosing Melanoma accurately and seemlessly.

Built With

• coreml
• machine-learning
• neural-networks
• swift
• xcode