Design of Automatic Tool for Diagnosis of Pneumonia Using Boosting Techniques

HIGHLIGHTS

• Automatic tool is developed for diagnosis of pneumonia. Boosting techniques are used in terms of their speed and ease of use in real time applications.

• The best result in terms of simulation duration and accuracy is Catboost with 0.7 seconds running time and 83% accuracy.

• The results obtained from the model become more understandable using tool.

• A bridge is designed between the model and the user by the automatic tool.

• By using this tool, a diagnosis can be done quickly and accurately without any expert. So, treatment can be started quickly.

Abstract

Covid-19 is today's pandemic disease and can cause the hospital crowded. Additionally, It affects the lungs and may cause pneumonia. The most popular technique for diagnosis of pneumonia is the evaluation of X-ray. However, a sufficient number of radiologists are needed to interpret the X-ray images. High rates of child deaths due to pneumonia have been encountered. Using this type of system, a diagnosis can be made quickly, and then the treatment process can be started rapidly. This study aims to diagnose pneumonia using boosting techniques by the automatic tool. With this tool, the workload of the doctors/radiologists is reduced. The boosting techniques are a family of machine learning techniques. Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost) are used for the study. These techniques are chosen because of their simulation duration for modeling and convenience for real-time applications. L2 normalization and feature selection are applied to the data before applying the techniques. Random Forest Classifier is used for feature selection estimator. After the modeling, Categorical Boosting algorithm is observed as faster than the other techniques. Simulation duration is obtained as 0.7 seconds. By using this automatic tool, the user can be able to upload the desired X-ray image to the system and get the result easily from the screen without any radiologist/doctor.

Keywords:
Categorical boosting; extreme gradient boosting; gradient boosting; light gradient boosting; machine learning; pneumonia; user interface tool