What steps did you take to develop your project?
First, we had to do our research on lung cancer. We began to research lung cancer and the types of symptoms, and we recorded everything that we learned in a notebook so that we could refer back to it during the creation of our app. We soon learned that there are different forms of lung cancer and we discovered that tumors can occur within the lungs. There are a few calculators that one can insert information that can help them figure out if they have lung cancer, but there is no app except SkinVision (an app that detects skin cancer using images of the external body) and LUNGevity (another app that allows for the communication between patients and doctors).
We took all of this information we had gathered and started mapping out a plan. We soon decided to make a survey that the user can take so that there is a better accuracy. We began to map out our survey, as well as the key information we would have to include in within the questions, such as “have you experienced any pain in your chest or ribs”. We always made sure that our questions correlated with symptoms of lung cancer. Also, we have been constantly adding questions to our survey so that we can always make the results more accurate. Continuing on, we finished mapping out our app – login page/sign up page, customer/ person using the app, survey, results page. Once we got our design and basic functions mapped out, we then started to create an actual prototype of the app. While we were creating the prototype, we also began to set up our image classification by collecting various kinds of images of lungs infected with tumors and healthy lungs.
Why are you competing?
Lung cancer is a huge problem in our world that is affecting millions of people worldwide. We needed to find a quick and reliable way for both patients and doctors to identify lung cancer effectively, so that it can be prevented from worsening. Because of this, we decided to create an app that detects lung cancer within people. It is a widely accessible app and can be used on Apple and Android devices, as well as desktops. It is unlike any other app that has been made and it combines the best qualities of the most efficient apps that detect various types of cancer. We were also able to incorporate image classification and sound classification in our app to greatly improve our accuracy. We also used JAVAscript coding as well as Python, C++ and Kotlin to help train our app with the different sounds and images. Our app is also an advancement of what is out there currently, and we plan to continue improving and expanding our app as more modern technology thrives. Lung cancer is a major occurring issue so we hope that you will join us in the journey of making a change in our world!