Apna Doctor

Apna Doctor

An AI based Health CheckUp web application that can help patients to know the disease and take precautions accordingly based on his/her symptoms and also connect virtually with doctors effectively.

The problem Apna Doctor solves

  • Whenever a patient visits a hospital it takes a significant amount of time before the updated health reports of the patient arrives making it difficult for the proper detection and hence decision making for the health official.

  • Also, it has now become unsafe to go to the hospital every time we feel unwell, since there is a risk of getting affected by COVID-19. The pandemic has caused an influx in hospital cases, and the limitations on hospital beds have people wondering whether their symptoms are severe enough to warrant a doctor's appointment.

  • Meanwhile, others experience ailments but are unable to afford a visit to the doctor due to a lack of or poor health care. Further, if the patient recognizes his/her symptoms, and if somehow we can tell him what is the disease he is likely to be affected with then he/she can take precautions accordingly at home only.

  • Thus our application provides a computer-aided diagnosis system to help doctors in the early identification/detection and diagnosis of various diseases such as Cancer, Heart disease, Alzheimer, Brain tumor, Covid19, Glaucoma etc. For this, it is required to enter the medical details of a patient or upload their X-Ray or MRI image to get prediction using machine or deep learning.

  • It provides feature for prediction of the disease based on symptoms (either simply type the symptoms or record the audio in browser) that the patient is experiencing and they will get to know what possible diseases they might have along with the precautions that they must take.

  • A doctor appointment system has been integrated wherein patients can not only search doctors based on region or specialization, but also connect virtually with the doctors around the globe using mail and video call feature.

Challenges we ran into

  • For improving the accuracy of ML/DL models, we used Random or Grid Search, Hyper-parameter tuning and on-the-fly augmentations to avoid overfitting concerns.

  • Handling this project with college classes was a test of our time management skills, but that's totally worth it at end.

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