NGBoost Classifier Using Deep Features for Pneumonia Chest X-Ray Classification
Source abstract
Abstract
Pneumonia remains a major global health concern, leading to significant mortality and morbidity. The identification of pneumonia by chest X-rays can be difficult due to its similarity to other lung disorders. In this paper, Natural Gradiant Boost (NGBoost) classifier is employed on deep features obtained from ResNet50 model to classify chest X-ray images as normal or pneumonia-affected. NGBoost classifier, a probabilistic machine learning model is used in this study to evaluate the discriminative power of handcrafted features like haar, shape and texture and deep features obtained from convolution neural network models like ResNet50, DenseNet121 and VGG16. The dataset used in this study is obtained from the pneumonia RSNA challenge, which consists of 26,684 chest X-ray images. The experimental results show that NGBoost classifier obtained an accuracy of 0.98 using deep features extracted from ResNet50 model. From the analysis, it is found that deep features play an important role in pneumonia chest X-ray classification.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: COVID-19 diagnosis using AI · Traditional Chinese Medicine Studies · Radiomics and Machine Learning in Medical Imaging
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Information Science and Engineering, JSS Academy of Technical Education, Bengaluru 560060, Karnataka, India
Department of Information Science and Engineering, Sri Jayachamarajendra College of Engineering, JSS Science and Technology University, Mysuru 570006, Karnataka, India
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