Overview / Usage

Pneumonia is the most common form of disease in human lungs, and Viral Pneumonia and Bacterial Pneumonia are the two major forms of Pneumonia that can cause severe damages to the human respiratory system which might lead to death if not treated correctly before it's too late. Therefore Accurately identifying and categorizing the pneumonia subtypes is an important and challenging clinical task, and automated methods can be used to save time and reduce error.

It becomes difficult for even experienced physicians and specialists to identify pneumonia from X-Ray images of patients. If left undetected for few weeks it might cause severe health issues in the patients.
Therefore we are using deep learning technologies to train Artificial Intelligence (AI) to be able to detect two classes of pneumonia (Bacterial and Viral Pneumonia). The Intel® Distribution of OpenVINO™ Toolkit helps in model optimisation and inference engine for the computer vision architecture.

Methodology / Approach

The data set is organized into 3 folders (train, test, val) and contains sub folders for each image category (Pneumonia/Normal). There are 5,863 X-Ray images (JPEG) and 2 categories (Pneumonia/Normal).

Chest X-ray images (anterior-posterior) were selected from retrospective cohorts of pediatric patients of one to five years old from Guangzhou Women and Children’s Medical Center, Guangzhou. All chest X-ray imaging was performed as part of patients’ routine clinical care.

For the analysis of chest x-ray images, all chest radio-graphs were initially screened for quality control by removing all low quality or unreadable scans. The diagnoses for the images were then graded by two expert physicians before being cleared for training the AI system. In order to account for any grading errors, the evaluation set was also checked by a third expert.

1. Create data directory
2. Label our dataset and create .xml file corresponding to each image.
3. Create train and test data directories. Create a script for generating the train.csv and test.csv for the data.
4. Generate the train.record and test.record files.
5. We will then train our classifier algorithm with the data using Tensorflow or Caffe using the Open Vino toolkit and create a model out of it.
6. Optimize our model to create an *.xml and *.bin file
7. Then we will create a setup using the Inference API so that it is easily gets optimized results on the CPU using the camera and finally we identify the pneumonia type given input of an x ray image.
8. Once the identification is done, then the model will provide other details like why the disease has happened, the possible cures, preventive measures etc.

Technologies Used

Hardwares Used :-

1. Intel Powered PC (Intel 7th Gen i5 NUC - NUC7i5BNH Barebone).
2. Intel AI DevCloud.
3. Movidius Neural Compute Stick for inference on edge devices.

Technologies Used :-

1. Intel Optimised Python.
2. Intel Optimised TensorFlow.
3. Intel® Distribution of OpenVINO™ toolkit.

Repository

https://drive.google.com/open?id=1fCrgjthZAddGtiOWGMpBwA-Sp39Ft-Mt

Other links

• Why Detecting Pneumonia is a challenge for physicians

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