## Inspiration

I was inspired by my parents. Both of my parents are doctors, and I thought that not all countries or regions have doctors well equipped with the knowledge to diagnose diseases in time, which leads to several deaths.

## What it does

My website helps heath works to diagnose diseases like pneumonia, heart disease, and malaria, with the help of AI and machine learning.

## How we built it

I but the site with the help of python, flask. I trained the models with the help of keras.

## Challenges we ran into

I ran into the challenge of making the interface friendly, and training models with selective variables which does not take much knowledge.

## Accomplishments that we're proud of

Finishing the project was a big accomplishment, I did not think I would be able to finish it alone.

## What we learned

I learned a lot about flask and front-end development because I had very less experience with it.

## What's next for DoDiagnosis

I would like to add more diagnosis tests, like skin cancer and heart strokes, which I would have liked to complete in the hackathon but due to time constraint was not able to.
## Inspiration

For years, the field of dermatology has been facing a myriad of challenges, some of which include the accuracy and efficiency of diagnosing skin conditions. Skin disease diagnoses were traditionally based on visual inspections, observations, and measurements. However, the accuracy of these methods will vary between healthcare professionals and dermatologists, depending on their experience and skills. As a result, skin conditions are often misdiagnosed, leading to severe consequences. With the advancement of Machine Learning in recent years, healthcare professionals and dermatologists can use this technology to gain faster and more precise results. Our team was inspired by the challenges that skin conditions pose, and by the immense potential that ML could bring to the field of dermatology.

## What Our Project Does

Our website is capable of classifying 5 different types of skin conditions (blackhead, cyst, whitehead, papule, and pustule) using selfies taken or uploaded by users. From there, we would provide users with tailored insights on how to alleviate the condition.

## How We Built It

We trained an acne classifier from 200 images using TensorFlow and Keras. Then, we built the back-end component using Flask and the front-end using React.

## Challenges We Ran Into

During the project, we encountered several issues

* For the ML model, we weren't able to find a lot of available quality images that could be used to train the model.
* At first, we planned to built the website using only React and encountered a huge obstacles with parsing JSON file since some Keras layers were not compatible with TensorFlow.js, so we decided to implement the back-end using Flask to solve the issue.
* Our team didn't have prior experience with back-end so we also faced some issues while implementing it.

## Accomplishments that we're proud of

Using only a very limited set of data, we managed to build our first ML model capable of classifying 5 different types of skin conditions, ***achieving an accuracy of more than 60%***

## What We Learned

As for the technical part, we learned about creating a Machine Learning model using TensorFlow and Keras, and about how to create a full-stack web app with Flask and React. Besides, we also acquired a lot of knowledge about skin conditions, especially the 5 types that we trained our model to classify.

## Future Improvements

* Further enhance the accuracy of our model with a better dataset.
* Train our model to classify the severity of skin conditions, skin types, and skin tones.
* Implement the feature to create personalized skincare regimes based on the given information.
* Develop a mobile app for the project.
* Improve the user experience by making it compatible with more devices.
## Inspiration

When visiting a clinic, two big complaints that we have are the long wait times and the necessity to use a kiosk that thousands of other people have already touched. We also know that certain methods of filling in information are not accessible to everyone (For example, someone with Parkinsons disease writing with a pen). In response to these problems, we created Touchless.

## What it does

* Touchless is an accessible and contact-free solution for gathering form information.
* Allows users to interact with forms using voices and touchless gestures.
* Users use different gestures to answer different questions.
* Ex. Raise 1-5 fingers for 1-5 inputs, or thumbs up and down for yes and no.
* Additionally, users are able to use voice for two-way interaction with the form. Either way, surface contact is eliminated.
* Applicable to doctor’s offices and clinics where germs are easily transferable and dangerous when people touch the same electronic devices.

## How we built it

* Gesture and voice components are written in Python.
* The gesture component uses OpenCV and Mediapipe to map out hand joint positions, where calculations could be done to determine hand symbols.
* SpeechRecognition recognizes user speech
* The form outputs audio back to the user by using pyttsx3 for text-to-speech, and beepy for alert noises.
* We use AWS Gateway to open a connection to a custom lambda function which has been assigned roles using AWS Iam Roles to restrict access. The lambda generates a secure key which it sends with the data from our form that has been routed using Flask, to our noSQL dynmaoDB database.

## Challenges we ran into

* Tried to set up a Cerner API for FHIR data, but had difficulty setting it up.
* As a result, we had to pivot towards using a noSQL database in AWS as our secure backend database for storing our patient data.

## Accomplishments we’re proud of

This was our whole team’s first time using gesture recognition and voice recognition, so it was an amazing learning experience for us. We’re proud that we managed to implement these features within our project at a level we consider effective.

## What we learned

We learned that FHIR is complicated. We ended up building a custom data workflow that was based on FHIR models we found online, but due to time constraints we did not implement certain headers and keys that make up industrial FHIR data objects.

## What’s next for Touchless

In the future, we would like to integrate the voice and gesture components more seamlessly into one rather than two separate components.