COVID-19-Detection-Based-On-Human-ChestXray

by Chandrateja Reddy

INTRODUCTION

1. Motivation

Chest radiography is the most common imaging examination globally, critical for screening, diagnosis, and management of many life threatening diseases.

My aim is to provide automated COVID-19 chest radiology analysis comparable to professional practicing radiologists. In addition, I hope to assist various medical settings such as improved workflow prioritization and clinical decision support. I have faith that my application will advance global population health initiatives through large-scale screening.

2. Plan

Main steps:

Task	Time	Progress
Data collection	1 days	x
Data preprocessing	1 days	x
Building Model	5 days	x
Build Flask App	1 day	x
Total	8 days

MATERIALS AND METHODS

1. Datasets

This dataset contains 1000 images of COVID-19 and Normal of 200 patients.I had Provided the COVID-19 dataset,Which all preprocessing done...!! dataset link: https://drive.google.com/drive/folders/1sIm7jJ_OcTIeNYfx_Yu54c_5whN0-UVL?usp=sharing

2. Methods

• Python and some neccessary libraries such Tensorflow, keras, pandas, numpy, keras, tensorflow, flask.
• Google Cloud Platform to train models.

3. Building Models

• The training labels in the dataset for each observation are either 0 (COVID +ve), 1 (Normal). Explore different approaches to using the uncertainty labels during the model training.

Binary classification CNN model to recognise 2 different medical observations corona +ve or -ve.

• Building CNN model

The architecture is built by Tensorflow and Transfer Learning techniques. More details can be found in COVID-19_detector.ipynb.

densenet = tf.keras.applications.densenet.DenseNet121(weights='imagenet',input_shape=(224,224,3),include_top=False)
densenet.trainable=False
                                                  
model = tf.keras.Sequential([
    densenet,
    tf.keras.layers.GlobalAveragePooling2D(),
    tf.keras.layers.Dropout(0.25),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(128, activation='elu'),
    tf.keras.layers.Dropout(0.5),
    tf.keras.layers.Dense(64, activation='elu'),
    tf.keras.layers.Dropout(0.5),
    tf.keras.layers.Dense(14, activation = 'sigmoid')])

base_learning_rate = 0.0001
model.compile(optimizer=tf.keras.optimizers.Adam(lr=base_learning_rate),
              loss='binary_crossentropy',
              metrics=["accuracy"])

4. Model performance summary

Our model has the AUC of ~97 % for the train dataset and ~90 % for the validation dataset.

5. UI

Pages

CONCLUSION

Successfully built a deep neural network model by implementing Convolutional Neural Network (CNN) to automatically interprete ChestX-ray_Based-COVID detection. In addition, we also built a Flask application so user can upload their X-ray images and interpret the results.