Using Convolutional Neural Network, I have implemented a classifier which detects whether the person is infected by Covid-19 or not.
- Positive Cases : https://github.com/ieee8023/covid-chestxray-dataset
- Normal Cases : https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia
Link : https://www.kaggle.com/fusicfenta/covid-19-detection-using-chest-x-ray (If you liked this notebook then don't forget to give upvote on kaggle.)
Model: "sequential_1"
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) (None, 222, 222, 32) 896
conv2d_2 (Conv2D) (None, 220, 220, 64) 18496
max_pooling2d_1 (MaxPooling2 (None, 110, 110, 64) 0
dropout_1 (Dropout) (None, 110, 110, 64) 0
conv2d_3 (Conv2D) (None, 108, 108, 64) 36928
max_pooling2d_2 (MaxPooling2 (None, 54, 54, 64) 0
dropout_2 (Dropout) (None, 54, 54, 64) 0
conv2d_4 (Conv2D) (None, 52, 52, 128) 73856
max_pooling2d_3 (MaxPooling2 (None, 26, 26, 128) 0
dropout_3 (Dropout) (None, 26, 26, 128) 0
conv2d_5 (Conv2D) (None, 24, 24, 128) 147584
max_pooling2d_4 (MaxPooling2 (None, 12, 12, 128) 0
dropout_4 (Dropout) (None, 12, 12, 128) 0
flatten_1 (Flatten) (None, 18432) 0
dense_1 (Dense) (None, 64) 1179712
dropout_5 (Dropout) (None, 64) 0
dense_2 (Dense) (None, 1) 65
=================================================================
Total params: 1,457,537
Trainable params: 1,457,537
Non-trainable params: 0
Steps to Execute the Project
1) Open CMD in directory where "app.py" is stored (Basically, Open CMD to this folder)
2) enter : python app.py
3) Open Specified URL which is given to you in CMD in running process
4) Upload Any Chest XRay Image and click "Predict"
5) To stop serving, press CTRL+C