Advantage of Artificial Intelligence to detect COVID 19 using Chest X-Ray.
- Conditions
- Coronavirus as the cause of diseases classified elsewhere,
- Registration Number
- CTRI/2020/08/027008
- Lead Sponsor
- Institute of Technology and Institute of Pharmacy NIRMA University
- Brief Summary
**AI** **based Medical** **D****i****a****gn****o****s****ti****c****S****y****s****tem**
**D****etec****t****i****o****n** **o****f COVID 19 through Chest radiography images**
In Collaboration with Nirma University and GSC Medical college
**T****abl****e** **of Contents**
**C****h****ap****t****e****r 1 Abstract**
**C****h****ap****t****e****r 2 Problem Description**
**C****h****ap****t****e****r 3 Data**
**C****h****ap****t****e****r 4 AI Framework**
**4.1 Classification network**
**4.2 Generating heatmap**
**C****h****ap****t****e****r 5 Model Details**
**5.1 Resnet50**
**5.2 Proposed model**
**5.3 Heatmap generation**
**C****h****ap****t****e****r 6 Additional Experiments**
**C****h****ap****t****e****r 7 Deployment**
**C****h****ap****t****e****r 8 Future Scope**
**Chapter 1 - Abstract:**
Rapid detection of severe acute respiratory syndrome coronavirus 2 (SARS CoV-2), which is responsible for coronavirus disease 2019 (COVID-19), using chest radiography images has life-saving importance for both patients and doctors. Collection of legitimate chest data along with labels is used to train a dense net model with a similar knowledge base whose results are further validated through severity heatmaps. We have had MOU agreement and ties with the Gujarat Cancer Society (GCS) which helps us by providing real world patient data so as to further improve our model classification accuracy.
**Chapter 2 - Problem Description:**
Due to the spread of the novel coronavirus, many lives are being lost as the virus is highly contagious. The most accurate test for Covid 19 in present is the RT-PCR test. But one disadvantage of this test is that it takes more than 24 hours for the results after the collection of the sample. This work felicitates a rapid and easy detection of the chances of a person having been infected with novel coronavirus by just analysis of the chest X- ray of the patient. By this at-least doctors can know if the patient should be quarantined until the results of his/her RT-PCR test or not.
**Chapter 3 - Data:**
For achieving the task of diagnosing COVID-19 from medical image analysis we collected and combined data from multiple sources and Additional Dataset was provided by the **Gujarat Cancer Society**
Final Dataset:
| | | |
| --- | --- | --- |
| **N****o****rma****l**
**C****O****VID-19**
| **T****ra****inin****g :**
**7966**
**973**
| **T****es****ting:**
**100**
**100**
| **To****tal:**
**8066**
**1073**
**To****tal Training Images: 8939 || Total Testing Images: 200**
**Chapter 4 - AI framework:**
We propose the following pipeline for the detection of COVID-19 from chest radiography images. The pipeline consists of two main modules:
**4****.****1 Classification network:**
We trained Resnet50, deep convolutional architecture with skip connections and identity blocks on the dataset we created by combining several open-source datasets from scratch, for the purpose of classification.
**Evaluation:-**
**T****es****t Accuracy: 85%**
To further improve the credibility of our results we propose transfer learning on the base model Che-x-Net which is trained on the base ChestX-ray14 dataset, which contains 112,120 frontal view X-ray images from 30,805 unique patients for detection of 14 diseases (mainly pneumonia). Since transfer learning allows us to use the knowledge gained from other tasks in order to tackle new but similar problems effectively, it can also aid our task. In our case, weights of feature extractor/convolutional blocks are kept the same as that of the Che-x-Net fully trained network. We remove the final output layer of the network and add a few dense layers followed by the output layer with 2 neurons (namely: Normal, COVID-19).
The loss function employed is categorical cross-entropy.
**Evaluation:**
**T****es****t Accuracy: 92%**
**4****.****2 Generating Heat map:**
For the ones classified as COVID19 positive our approach highlights evidence i.e. diseased patches in the radiography images for clinical users to ease their decision to accept or reject a deep learning-based chest radiography diagnosis.
**Chapter 5 - Model Details:**
**5****.****1 ResNet50 (Residual networks):**
A convolutional neural network that is 50 layers deep. It employs skip connections that mitigate the problem of vanishing gradients by allowing an alternate shortcut path for the gradient to flow. The ResNet-50 model consists of 5 stages each with a convolution and identity block. Each convolution block has 3 convolution layers and each identity block also has 3 convolution layers.
**5****.****2 Proposed model - COVID19Net:**
COVID19Net is a 121-layer Dense Convolutional Network (DenseNet) pretrained on the ChestX-ray14 dataset and extended for the dataset created by us for COVID19 diagnosis. DenseNets improve the flow of information and gradients through the network, making the optimization of very deep networks tractable.The final layer is replaced by a few dense layers to be trained on our data. The final 2-neuron dense layer is followed by a softmax function for getting the classification probability.The network is trained end-to-end using Adam with standard parameters ( β1 = 0.9 and β2 = 0.999).We train the model using mini- batches of size 16. We use an initial learning rate of 0.001 that is decayed by a factor of 10 each time the validation loss flattens after an epoch, and pick the model with the lowest validation loss.
**5****.****3 Heat map generation:**
We employ a prediction difference method for the visualization of trained models. We find the difference between the pixel values of the image patches in predicted images and image patches in the normal baseline radiography images. This approach generates a relevance score for each pixel which is visualized as a heat map.
**Chapter 6 - Additional Experiments:**
1) Data Pre-processing:
As training data was collected from many different sources it was beneficial to pre- process all the images before feeding them into the network.
Here, we used 2 types of Data Pre-Processing methods:
A) Gamma - Correction
B) Image Histogram Equalization
2) Use of Weighted Loss Function:
To overcome the high imbalance in the data-set which contained nearly 8000 normal images and only 1073 covid 19 images we employed a weighted loss function which gave more weight to the class with less number of data points so as to stop the network from being biased to one class.
3) Use of Balanced Data-Set:
To effectively train the model we also tried using random 1073 images from the Normal Class which resulted in the same number of images in both the classes ie Normal and Covid 19. By this, the model got trained on a perfectly balanced dataset.
**A****fter all the Experiments the final Test Accuracy obtained is 97%**
**Chapter 7 - Deployment:**
â— To deploy the model in real-time a web application is prepared with the use of a flask framework. And the model is deployed on an Nvidia V-100 GPU.
â— Additionally, a cropping function is provided for the user to extract the actual area of interest from the chest X-ray.
**Chapter 8 - Future Scope:**â—
With increasing size of accurate data the model should be constantly updated/trained to get an improving classification accuracy.
â— Can also include CT-scans and severity segmentation maps for a holistic medical system development.
â— To collect and store medical details of patients and suggest them possible check- ups and remedies on a timely basis.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- Not Yet Recruiting
- Sex
- All
- Target Recruitment
- 1000
Chest x-rays taken in department radiodiagnosis, GCSMC.
Not provided
Study & Design
- Study Type
- Observational
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method To compare the sensitivity of artificial intelligence in detection of COVID 19 using chest x rays to human radiologist. 2 months
- Secondary Outcome Measures
Name Time Method To know prevalence of COVID 19 using artificial intelligence in different age and sex groups 2 months
Trial Locations
- Locations (1)
GCS Medical College and Hospital
🇮🇳Ahmadabad, GUJARAT, India
GCS Medical College and Hospital🇮🇳Ahmadabad, GUJARAT, IndiaDr Asutosh N DavePrincipal investigator9825038648drasutosh@yahoo.com