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Pneumonia accounts for over 15% of all deaths of children under 5 years old internationally. In 2015, 920,000 children under the age of 5 died from the disease.
Why: Problem statement
While common, accurately diagnosing pneumonia is a tall order. It requires the review of a chest radiograph (CXR) by highly trained specialists and confirmation through clinical history, vital signs, and laboratory exams. Pneumonia usually manifests as an area or areas of increased opacity on CXR. However, the diagnosis of pneumonia on CXR is complicated because of several other conditions in the lungs such as fluid overload (pulmonary edema), bleeding, volume loss (atelectasis or collapse), lung cancer, or post-radiation or surgical changes. Outside of the lungs, fluid in the pleural space (pleural effusion) also appears as increased opacity on CXR.
How: Solution description
In order to reduce this problem, we need to build an algorithm to detect a visual signal for pneumonia in medical images. Specifically, our algorithm needs to automatically locate lung opacities on chest radiographs. The block diagram is shown below.
Let’s have a look at the data:
The detailed class info dataset contain 28989 rows with 2 columns and train label dataset contain 28989 rows with 6 columns.
The detailed info dataset contain patient ID, target (either 0 or 1 for absence or presence of pneumonia respectively). The train labels dataset contain the patient ID, and the corresponding abnormality bounding box defined by the upper-left hand corner (x, y) coordinate and its corresponding width and height. In some case, the patient does not have pneumonia is set to NaN.
Let's check the class distribution from class detailed info:
Let's look into more details to the classes.
People with no lung capacity / Not normal are 39.67%, people with lung opacity are 30.92% and the normal people are 29.4%.
In the train set, the percent of data with value for Target = 1 is therefore 30.92%.
Merge class detail info and train data:
Let’s plot the graph between target value:
No lung opacity / Not normal / Normal is said to be 0 and lung opacity is said to be 1.
Detection of lung opacity window:
For the class Lung Opacity, corresponding to values of Target = 1, we plot the density of x, y, width and height.
We can also plot the center of the rectangle points in the plane x0y plane. The center of the rectangle is calculated by,
Xc = x + width / 2
Yc = y + height / 2
Now extract one image and process the DICOM information.
We can observe that we do have available some useful information in the DICOM metadata with predictive value, for example:
Patient sex; Patient age; Modality; Body part examined; View position; Rows & Columns; Pixel Spacing.
Let's sample few images having the Target = 1.
Plot DICOM images with Target = 1
For some of the images with Target=1, we might see multiple areas (boxes/rectangles) with Lung Opacity.
Let's sample few images having the Target = 0.
How is it different from competition
This project will be useful for everyone. Once the people able to detect the pneumonia, then they will be able to recover it quickly.
Who are your customers
Patients who are affected from lug disease like pneumonia.
Doctors can use this project to predict patient's disease.
Data Scientist can use this for study and literature survey.