Presentation
Viral Pneumonia Disease Classification with Machine Learning Techniques
DescriptionPneumonia is a dreadful condition that is the primary cause of death globally for individuals of all ages, but it is especially dangerous for small children who are younger than five. The radiological results obtained from an X-ray could lead to mistakes, incorrect diagnoses, and unnecessary delays. Datasets with chest X-ray were acquired from a Hopkins Diagnostic Center and 10 classifiers were applied. This work aims to develop ensemble machine and transfer learning models to classify viral pneumonia disease and apply ensemble techniques to the models. The models incorporate a variety of machine learning approaches, including k-nearest neighbors (KNN), decision tree (DT), random forest (RF), logistic regression (LR), and support vector machine (SVM). Furthermore, the transfer learning approach is used on the deep learning architectures VGG-19, DenseNet-121, GoogLeNet, AlexNet, and MobileNet-V2. The Keras code backend was implemented using TensorFlow.
On the general model’s performance: The model's output using the local dataset with 1,113 has the SVM, KNN, RF, LR, AlexNet, and GoogLeNet with 97%, 98%, 95%, 94%, 99%, and 100%, respectively, as the best in their performances; while DT, MobileNet, VGG-19, and DenseNet, with 89%, 80%, 77%, and 70% respectively, are the lowest in performance. The Max Voting Ensemble yielded 97% and weighted average yielded 98%. The results obtained from the analysis revealed the highest performance classification abilities of the seven models. The classification models for viral pneumonia disease enhance clinical practice by enabling improved interpretation of results, early prediction, detection, and life-saving interventions.
On the general model’s performance: The model's output using the local dataset with 1,113 has the SVM, KNN, RF, LR, AlexNet, and GoogLeNet with 97%, 98%, 95%, 94%, 99%, and 100%, respectively, as the best in their performances; while DT, MobileNet, VGG-19, and DenseNet, with 89%, 80%, 77%, and 70% respectively, are the lowest in performance. The Max Voting Ensemble yielded 97% and weighted average yielded 98%. The results obtained from the analysis revealed the highest performance classification abilities of the seven models. The classification models for viral pneumonia disease enhance clinical practice by enabling improved interpretation of results, early prediction, detection, and life-saving interventions.
Event Type
Doctoral Showcase
TimeThursday, 20 November 20254:45pm - 5:00pm CST
Location230
Archive
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