So guys here comes one of the most advanced data science medical projects that I have done till now in my whole Data Science journey. It is called HealthCure which is an all-in-one medical solution. We know the future is all about AI so here is my idea of bringing 7 disease detections under one platform using the power of AI. Although these results are perfect yet an experiment we did, which can prove to be a revolution in the coming years.
The main advantage of this project is that we can get the test results immediately at our home with a just few clicks.
7 disease detections:
- Covid-19 Detection
- Brain Tumour Detection
- Breast Cancer Detection
- Alzheimer Detection
- Diabetes Detection
- Pneumonia Detection
- Heart Disease Detection
A quick recap of Convolutional Neural Networks…
- A Convolutional Neural Network (ConvNet/CNN) is a Deep Learning algorithm that can take in an input image, assign importance (learnable weights and biases) to various aspects/objects in the image, and be able to differentiate one from the other.
- The pre-processing required in a ConvNet is much lower as compared to other classification algorithms. While in primitive methods filters are hand-engineered, with enough training, ConvNets have the ability to learn these filters/characteristics.
- The architecture of a ConvNet is analogous to that of the connectivity pattern of Neurons in the Human Brain and was inspired by the organization of the Visual Cortex.
- A ConvNet is able to successfully capture the Spatial and Temporal dependencies in an image through the application of relevant filters.
- The role of ConvNet is to reduce the images into a form that is easier to process, without losing features that are critical for getting a good prediction.
All Disease Detections…
- Used custom-made CNN architecture for this detection.
- The accuracy achieved was around 93%.
Brain Tumour detection
- Used VGG-16 for feature extraction.
- Used custom-made CNN ahead of CNN.
- The accuracy achieved was around 100% ?(just tested on 10 images).
Breast Cancer Detection
- Used Random Forest for this use case.
- The accuracy achieved was around 91.81%.
- Trained CNN architecture for this use case.
- The accuracy achieved was around 73.54%.
- Used Random Forest for this use case.
- The accuracy achieved was around 66.8%.
- Used custom CNN architecture for this use case.
- The accuracy achieved was around 83.17%.
Heart Disease Detection
- Used XGBoost for this use case.
- The accuracy achieved was around 86.96%.
- As time passes, we will be available with more and more data and we will try to make our models even more accurate by training on much more data.
- Also, we will be adding more disease detections that can be detected using X-ray scans or just by inputting simple numbers.
- We are also planning to add more features like if a person is found positive then our app will show him what precautions he needs to take and how he can cure himself.
- We will also be storing the detection records.
- So these are some future improvements/additions we plan to add.
How to run the project
Checkout the video tutorial here – https://youtu.be/psaMKTPdgIo
Download the app from here
Create a conda environment and install the required libraries
conda create -n healthcure python=3.9 conda activate healthcure pip install opencv-python numpy tensorflow scikit-learn imutils flask xgboost
When you have successfully created the environment, installed the required libraries, and activated it, simply run the following command in the terminal.
NOTE – This was my college major project which I did with the help of my 1 more teammate Yash Kelkar.
About Training Code, PPT, and Project Report…
Also, I am getting a very high number of requests for Training Files, PPT, and Project reports. So I have finally decided to share those with the people who are in real need. But as you know quality doesn’t come for free…
So, If you are really interested and want all the resources which include Training Code + App + PPT + Project Report, you have to pay a minimalistic fee of ₹500 and send me the payment screenshot at email@example.com.
You can Pay using the button below…
Or you can visit the Buy Projects page in the Main Menu bar…
People outside India can use Paypal to pay me – paypal.me/sharmaji270. Every ₹500 project costs $7 for International clients.
Check out the Reviews here…
Do let me know if there’s any query regarding data science projects or medical projects by contacting me via email or LinkedIn.
So this is all for this blog folks, thanks for reading it and I hope you are taking something with you after reading this and till the next time …
Read my previous post: OBJECT DETECTION USING SSD
Check out my other machine learning projects, deep learning projects, computer vision projects, NLP projects, and Flask projects at machinelearningprojects.net
37 thoughts on “HealthCure – an all in one medical solution – medical project – 7 disease detections – 2023”
hey! I really love your project but I can’t download the code.
Didn’t you made a documentation (Thesis) for this project??
If yes, then how can I avail that document??
Sorry, I don’t have anything like that at the moment…
is it possible to get a video on how to run this?
Just follow the steps shown in the blog…
in which ide should we run this project?
That doesn’t matter, I run my projects from terminal.
From where did you get the required data for training and testing of models??
There was no single source. I collected it from all over the internet.
For Alzheimer Detection which algorithm is used Random Forest or CNN ,i’m confused because in code you are using cnn architecture
That was a typo, used CNN for this use case.
Actually the Alzheimer model executed successfully but the output is always the milddemented when I change the value of ‘r’ that time only it gives me the desire output but can’t we get the desire output without changing the value of r?
You can try different things and keep what suits you best…
Hey, I am not able to run the code as in i am not getting the flow how to run it. Could you please help me with it
Hello! this is incredibly a Great project. Can you please specify which terminal did you work on for this and also about the conda environment.
You can work on any terminal and in any environment 🙂
is this an offline desktop app or a web app?
A desktop app that will run on localhost…
BTW you can deploy it online on Heroku or pythonanywhere for free also…
Can this Analysis be done on jupyter? Thanks
This is a flask app mate. You need to use a terminal in any way to execute it and see its front end. Meanwhile, the training part can be done on Jupyter.
Hey, How can i connect you regarding this project training code ppt and other stuff
Contact me on firstname.lastname@example.org
Are we using any database here to display the patient details at the result page?
We can use but here I have not used any database. It just makes a prediction in the background and goes to the result page with a POST request.
How to pay 500₹ your gpay or bank account is not mentioned.
Although the details are present here – https://machinelearningprojects.net/payment-page/
You can pay me on my UPI – abhi70420@paytm
Sir, I have downloaded the project and everything but when i type flask run then it is showing “could not locate a flask application” ..
Run the terminal in the downloaded folder where app.py is located
Hello Abhishek Sharma , I want this app with some detils Can you help
Mail me at email@example.com
Where are the machine learning files that you used to test ?
It’s in the models folder. You can download the compressed file.
I would like to get the Training Code, PPT, and Project Report for your Healthcure project however I am outside of India so I cant use PayTm is there some other platform like PayPal which i can send the funds to you to receive those files thank you.
does this app use any authentication or db credential ?
Not till now…