KO | EN

GitLite

Minimalist Open Source Repository Search Engine

gitlite — search

#typescript #ai-agents #developer-tools #codex #claude-code #cli #react #mcp #open-source #deepseek-harness #dsh-plugin #ai

Covid19-Detection-Using-Chest-X-Ray

★ 45 Open GitHub ↗

Covid-19 detection in chest x-ray images using Convolution Neural Network.

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Covid19-Detection-Using-Chest-X-Ray
Using Convolutional Neural Network, I have implemented a classifier which detects whether the person is infected by Covid-19 or not.

# Dataset
  1) Positive Cases : https://github.com/ieee8023/covid-chestxray-dataset
  2) Normal Cases : https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia

# Kaggle Notebook Link (Support Please 👍👍👍)
  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.)
# Network Architecture
    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
    
## Accuracy
![](https://raw.githubusercontent.com/11fenil11/Covid19-Detection-Using-Chest-X-Ray/master/MediaImages/Accuracy.PNG)

## Confusion Matrix (0 for Positive and 1 for Negative cases)
![](https://raw.githubusercontent.com/11fenil11/Covid19-Detection-Using-Chest-X-Ray/master/MediaImages/New_Confusion_Matrix.PNG)

## Prediction of Covid-19 Positive Case
![](https://raw.githubusercontent.com/11fenil11/Covid19-Detection-Using-Chest-X-Ray/master/MediaImages/Poisi_Out.PNG)

## Prediction of Covid-19 Negative Case
![](https://raw.githubusercontent.com/11fenil11/Covid19-Detection-Using-Chest-X-Ray/master/MediaImages/Negi_Out.PNG)

# Run
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

Covid19-Detection-Using-Chest-X-Ray

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README