SiddharthRajpal/HealthVision
This project uses deep learning algorithms and the Keras library to determine if a person has certain diseases or not from their chest x-rays and other scans. The trained model is displayed using Streamlit, which enables the user to upload an image and receive instant feedback.
HealthVision
This project uses deep learning algorithms and the Keras library to determine if a person has certain diseases or not from their chest x-rays and other scans. The trained model is displayed using Streamlit, which enables the user to upload an image and receive instant feedback.
Essentially it uses 6 different CNN models to diagnose 6 different diseases :- Covid, Glaucoma, Skin Cancer, Pneumonia, Tuberculosis and Brain Tumor classification
Covid and Pnemonia are detected using chest X-ray scans of the patient while Gluacoma uses an internal scan of the eye and Skin Cancer is detected
using external pictures
Trailer
Watch our Trailer here :- [https://drive.google.com/file/d/1rsuZLO70enTyi4aTBziKADpSQ04UE9KP/view?usp=sharing]
Our Model
Below is the summary of our CNN model which we have made using python and keras.
Model for Covid & Pneumonia Classification
| Layer (type) | Output Shape | Param # |
|---|---|---|
| conv2d (Conv2D) | (None, 222, 222, 32) | 896 |
| max_pooling2d (MaxPooling2D) | (None, 111, 111, 32) | 0 |
| conv2d_1 (Conv2D) | (None, 109, 109, 64) | 18496 |
| max_pooling2d_1 (MaxPooling2D) | (None, 54, 54, 64) | 0 |
| conv2d_2 (Conv2D) | (None, 52, 52, 128) | 73856 |
| max_pooling2d_2 (MaxPooling2D) | (None, 26, 26, 128) | 0 |
| flatten (Flatten) | (None, 86528) | 0 |
| dense (Dense) | (None, 128) | 11075712 |
As you can see this is a simple Neural Network with 8 layers. (this had an accuracy of approx 0.93 on average)
Each Layer has a specific job in order to get the desired output, they are :-
-
Convolutional layer (conv2d): This layer takes an input image and applies a set of 32 filters to produce 32 output feature maps. Each filter extracts a particular feature from the image. The output of this layer has a shape of (None, 222, 222, 32).
-
Max pooling layer (max_pooling2d): This layer reduces the dimensionality of the output of the previous layer by taking the maximum value in each 2x2 region. The output of this layer has a shape of (None, 111, 111, 32).
-
Convolutional layer (conv2d_1): This layer applies a set of 64 filters to the output of the previous layer to produce 64 output feature maps. The output of this layer has a shape of (None, 109, 109, 64).
-
Max pooling layer (max_pooling2d_1): This layer reduces the dimensionality of the output of the previous layer by taking the maximum value in each 2x2 region. The output of this layer has a shape of (None, 54, 54, 64).
-
Convolutional layer (conv2d_2): This layer applies a set of 128 filters to the output of the previous layer to produce 128 output feature maps. The output of this layer has a shape of (None, 52, 52, 128).
-
Max pooling layer (max_pooling2d_2): This layer reduces the dimensionality of the output of the previous layer by taking the maximum value in each 2x2 region. The output of this layer has a shape of (None, 26, 26, 128).
-
Flatten layer (flatten): This layer flattens the output of the previous layer into a 1D vector. The output of this layer has a shape of (None, 86528).
-
Fully connected layer (dense): This layer takes the flattened vector from the previous layer and applies 128 neurons to it, producing a 128-dimensional output. The output of this layer has a shape of (None, 128).
Examples
Covid-19
Positive
Negative
Glaucoma
Positive
itive
Negative
Pneumonia
Positive
Negative
Skin Cancer
Positive (Malignant)
Negative (Benign)
Usage
Our Site is hosted at this link :- [https://healthvisionai.streamlit.app/]
- Also here are some "Executors" for you to use while testing our software.. these are just references and you can use any image you have :- [https://github.com/SiddharthRajpal/HealthVision/tree/main/Executors]