The risk of pneumonia is immense for many, especially in developing nations where billions face energy poverty and rely on polluting forms of energy. The WHO estimates that over 4 million premature deaths occur annually from household air pollution-related diseases including pneumonia. Over 150 million people get infected with pneumonia on an annual basis especially children under 5 years old. In such regions, the problem can be further aggravated due to the dearth of medical resources and personnel. For example, in Africa’s 57 nations, a gap of 2.3 million doctors and nurses exists. For these populations, accurate and fast diagnosis means everything. It can guarantee timely access to treatment and save much needed time and money for those already experiencing poverty.
What it does
It is an algorithm to automatically identify whether a patient is suffering from pneumonia or not by looking at chest X-ray images. The algorithm had to be extremely accurate because lives of people is at stake.
How we built it
Environment and tools scikit-learn keras numpy pandas matplotlib
Dataset Used https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia
Challenges we ran into
It is our first hackathon. We have learnt from workshops and tried to implement the knowledge. As these things are new to us. So, It was very difficult for us to work on the project.
Accomplishments that we're proud of
We have learnt a lot by participating in this hackathon. We have build a model to detect Pneumonia from X-ray Images It can be tuned and trained on more images to be deployed.
What we learned
We have learnt about machine learning models, it's working and a lot like Teachable Machine, Shashido.
What's next for Detecting Pneumonia from X-ray Images
Model needs to be trained on more images and needs to be fine tuned for a robust model
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