Impact Of Machine Learning Models In Pneumonia Diagnosis With Features Extracted From Chest X-Rays Using VGG16

Abstract

Pneumonia is a viral, bacterial, or fungal infection that leads to the accumulation of pus or fluids in the alveoli of lungs causing breathlessness, lung abscess, or even death at later stages. Pneumonia is affecting a huge population across the globe. A quite large number of child deaths due to pneumonia are recorded which is significantly greater than death due to AIDS, malaria, and measles. Pneumonia diagnosis is considered one of the high priority research areas in Biomedicine. In this paper, a detailed comparative study was performed using various machine learning algorithms namely Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM). These models are trained with features extracted by a pre-trained deep convolutional neural network (DCNN), VGG16 for the diagnosis of pneumonia from chest x-rays. The combination of VGG16 along with Machine learning models witnessed a considerable improvement in accuracy with reduction in time consumed for training against the usage of DCNN models for prediction. The results of various machine learning models are fine-tuned by modifying the hyper parameters. By comparison, SVM with RBF kernel is identified to perform better than other classifiers.