Inspiration
Pneumonia and other lower respiratory tract infections are the leading cause of death worldwide. Exposure and inhalation of the contaminated air in these environments ultimately leads to inflammation, and fluid filling in the lungs, in turn, reducing oxygen flow to the bloodstream . Aspiration pneumonia, pneumonia acquired by patients in hospitals (via contact with ventilators, instruments) are other categories of pneumonia. Viruses are the primary cause of pneumonia in children under five years . Children, infants, elderly, people with weakened immune systems, and people with severe alcohol misuse have an increased risk. Additionally, MRI scans and imaging facilities are expensive and obtaining an accurate diagnosis is cumbersome. Patients in fast-growing metropolitan cities have better access to diagnostic imaging facilities, whereas those in third world countries or rural areas and
What it does
The iOS application is developed keeping in mind the solution irrespective of the platform. The application uses the Apple Developer provided API’s and training the CreateML with CoreML model. The Application has an option for the user to click or upload Chest X-RAY images while accessing the mobile gallery as well. The format of the image input is specified and the app is integrated with InceptionV3 to accept only specific format. The application then analyzes the input image and gives a result pop up notification. If the person is diagnosed positive, they are redirected to the screen with doctor and hospital references which are responded using the person’s geo-location. If they are not diagnosed with Pneumonia, they are redirected to screen with Symptoms, Precautions and Preventions.
How we built it
Pneumonia Disease Detection has been implemented by using a dataset of 157 X-ray images, of which 73 are X-ray images of pneumonia patients while 84 are X- ray images of healthy individuals, through google search engine database, i.e. from Kaggle.com Datasets.
Challenges we ran into
The hardest part was to collect the right amount of dataset to bring the perfectly feasible accuracy.
Accomplishments that we're proud of
Our research paper on this application is soon to be published in IEEE-SEM.
What we learned
This was our first experience of collaborating ML Models with an iOS Application
What's next for Pneumonia Detection Application
We are in talks with Lal Path Labs to implement this application in collaboration with them.
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