Classification of COVID-19 Disease From Chest X-Ray Images Using the SqueezeNet and Bayesian Optimization Methods

Rima Tri Wahyuningrum, Ach Halimi Firdaus Zuhron, Cucun Very Angkoso, Budi Dwi Satoto, Amillia Kartika Sari, Anggraini Dwi Sensusiati

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

COVID-19 is an infectious disease caused by a viral pathogen. The rapidity of COVID-19's transmission has led to its global pandemic status, affecting numerous countries. Consequently, there is a pressing need for an expeditious and efficacious diagnostic method. According to experts, radiographic images, specifically X-ray images, exhibit ground-glass opacities that can be leveraged for COVID-19 diagnosis. This study entails a dataset categorized into three distinct classes: COVID-19, pneumonia, and normal cases. The classification procedure harnesses Convolutional Neural Networks (CNNs) employing the Squeezenet-Bayesian optimization architecture, herein referred to as COVIDiagnosis-Net. COVIDiagnosis-Net represents a fusion of the SqueezeNet architecture and Bayesian optimization. Bayesian optimization facilitates online training to determine optimal hyperparameters within the SqueezeNet architecture. Once the optimal hyperparameters are established, model training commences to yield the finest model. Our research utilizes a dataset sourced from the Kaggle repository, comprising a total of 4,945 data instances. Subsequently, this dataset is partitioned into three distinct subsets: validation data, training data, and test data, with an 80% training data, 20% test data, and 20% validation data split derived from the training dataset. Upon conducting rigorous experimentation, our model achieved an accuracy rate of 93% and an Fl-Score of 93.5%. Additionally, through evaluation with Receiver Operating Characteristic (ROC) analysis, we obtained an average Area Under Curve (AUC) value of 0.9888 or 98.88%. Interpreting the AUC value, the COVIDiagnosis-Net model can be classified as highly proficient, given its AUC exceeding 0.9(90%).

Original languageEnglish
Title of host publication2023 8th International Conference on Informatics and Computing, ICIC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350342604
DOIs
Publication statusPublished - 2023
Event8th International Conference on Informatics and Computing, ICIC 2023 - Hybrid, Malang, Indonesia
Duration: 8 Dec 20239 Dec 2023

Publication series

Name2023 8th International Conference on Informatics and Computing, ICIC 2023

Conference

Conference8th International Conference on Informatics and Computing, ICIC 2023
Country/TerritoryIndonesia
CityHybrid, Malang
Period8/12/239/12/23

Keywords

  • Bayesian Optimazation
  • Convolutional Neural Network
  • COVID-19
  • COVIDiagnosis-Net
  • Image Classification
  • Squeezenet

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