Pneumonia Prediction Based On CXR Images Using Transfer Learning

Pneumonia Prediction Based On CXR Images Using Transfer Learning

In this article, we will build a classification model for predicting pneumonia and normal patient using Chest X Rays and Transfer Learning. Deep Learning has shown immense results in medical imaging. It is due to the high volume of data that is generated in the medical domain.

Deep Learning has shown immense results in medical imaging. It is due to the high volume of data that is generated in the medical domain. There are several use cases where AI technologies are used today in the healthcare domain. There can be errors made by humans depending on several factors whereas a machine will not make error provided data is correct. The reason for using Deep learning in medical imaging is the fact that we can attain insights from the data quickly with reliable results. With AI it has now become possible to detect even different types of cancer in the lungs and kidneys also it is used in different therapy. Diagnosing pneumonia is also one of the important applications of deep learning.

Pneumonia is such an infection that is caused in one or both the lungs. This is caused due to viruses, fungi, etc. This results in inflammation in air sacs in the lungs by which it becomes difficult to breathe. Through this article, we will explore how to build a classification model by which we can classify whether a person has pneumonia or not through CXR (Chest X-Ray) images. We will be building the model using pre-trained model Vgg19. For this experiment, we will make use of Pneumonia Chest X Rays data that is publicly available on Kaggle. 

There are a total of 5863 CXR (Chest X-Ray) images that are categorized into two categories that are Pneumonia and Normal. The data has three folders: train, test, and Val in which both two categories subfolders are present. The X-rays images were screened by experts so that there are no unreadable images or low-quality images. 

PIN IT

The normal chest X-ray (left panel) depicts clear lungs without any areas of abnormal opacification in the image. Bacterial pneumonia (middle) typically exhibits a focal lobar consolidation, in this case in the right upper lobe (white arrows), whereas viral pneumonia (right) manifests with a more diffuse ‘‘interstitial’’ pattern in both lungs. Read more here in this paper.

*Model Building *

First, we need to install the required package and libraries that are required. As we will be importing the data using the API command from Kaggle. We should have the Kaggle package installed. Use the below code for the same.

developers corner pneumonia pneumonia prediction transfer learning vgg19

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