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.
Highlights
Pneumonia is a lung infection caused due to the accumulation of fluids or pus in the alveoli of the lungs
A comprehensive study of using machine learning (ML) algorithms namely Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM) with different parametric values trained with features extracted by VGG16 deep convolutional neural network (DCNN) is presented in this paper
For SVM the experiments were conducted by comparing ‘Radial Basis Function (RBF)’, ‘linear’, 'Polynomial' and ‘sigmoid’ kernels, for experiments related to RF the number of trees was varied from 50 to 200, and LR is compared based on ‘L1’ and ‘L2’ penalizations
Summary
Pneumonia is a lung infection caused due to the accumulation of fluids or pus in the alveoli (air sacs) of the lungs. Chest x-rays are the most widely used imaging techniques than CT scans for the diagnosis of pneumonia [6 2001]. X-ray imaging is the most widely used technique that is playing an important role in epidemiological studies and clinical care [Cherian et-al 2005, [8] 2001]. With the advancements in computerized methods like deep learning (DL) and machine learning (ML), there were robust improvements in the automatic diagnosis of diseases. Impact Of Machine Learning Models In Pneumonia Diagnosis With Features Extracted From Chest XRays Using VGG16 et-al [Qin et-al 2018] performed a survey on using ML and DL techniques to diagnose diseases from chest xrays. A study was performed on using machine learning algorithms namely random forest (RF), logistic regression (LR) and support vector machine (SVM) trained with features extracted by a DCNN named VGG16 to diagnose pneumonia from chest x-ray images. SVM with RBF kernel reported the highest performance than other model configurations
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