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DTSTART;TZID=America/Chicago:20251118T080000
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UID:submissions.supercomputing.org_SC25_sess537_drs107@linklings.com
SUMMARY:Viral Pneumonia Disease Classification with Machine Learning Techn
 iques
DESCRIPTION:Racheal Shade Akinbo, Olabode Olatubosun, and Emmanuel Ibam (F
 ederal University of Technology Akure Nigeria)\n\nPneumonia is a dreadful 
 condition that is the primary cause of death globally for individuals of a
 ll 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 w
 ere applied. This work aims to develop ensemble machine and transfer learn
 ing models to classify viral pneumonia disease and apply ensemble techniqu
 es to the models. The models incorporate a variety of machine learning app
 roaches, including k-nearest neighbors (KNN), decision tree (DT), random f
 orest (RF), logistic regression (LR), and support vector machine (SVM). Fu
 rthermore, the transfer learning approach is used on the deep learning arc
 hitectures VGG-19, DenseNet-121, GoogLeNet, AlexNet, and MobileNet-V2. The
  Keras code backend was implemented using TensorFlow. \n\nOn the general m
 odel’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 we
 ighted average yielded 98%. The results obtained from the analysis reveale
 d the highest performance classification abilities of the seven models. Th
 e classification models for viral pneumonia disease enhance clinical pract
 ice by enabling improved interpretation of results, early prediction, dete
 ction, and life-saving interventions.\n\nTag: Research & ACM SRC Posters\n
 \nRecording: Not Livestreamed, Not Recorded\n\nRegistration Category: Tech
 nical Program Reg Pass\n\nSession Chairs: Kento Sato (RIKEN Center for Com
 putational Science (R-CCS)); Chris Schlipalius (Pawsey Supercomputing Rese
 arch Centre; Commonwealth Scientific and Industrial Research Organisation 
 (CSIRO), Australia); and Anja Gerbes (Georg-August-Universität Göttingen)\
 n\n
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