A new COVID-19 classification approach based on Bayesian optimization SVM kernel using chest X-ray datasets

Lakshmi, M.; Das, Raja; Manohar, Balakrishnama

doi:10.1007/s12530-024-09575-8

Evolving Systems

2024

DOI: 10.1007/s12530-024-09575-8

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A new COVID-19 classification approach based on Bayesian optimization SVM kernel using chest X-ray datasets

M. Lakshmi,

Raja Das,

Balakrishnama Manohar

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2024

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Cited by 3 publications

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Intelligent Bayesian Inference for Multiclass Lung Infection Diagnosis: Network Analysis of Ranked Gray Level Co-occurrence (GLCM) Features

Khan,

Majid,

Shim

et al. 2024

New Gener. Comput.

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Intelligent Bayesian Inference for Multiclass Lung Infection Diagnosis: Network Analysis of Ranked Gray Level Co-occurrence (GLCM) Features

Khan,

Majid,

Shim

et al. 2024

New Gener. Comput.

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A hybridization of multiple imputation and one-class bagging ensemble approach for missing value and class imbalance problem

Baro,

Borah

2024

Evolving Systems

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Deep Learning-Based Diagnosis of Pneumonia Using Convolutional Neural Networks

Mudiyanselage

2024

J. Comput. Mech. Manag

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Pneumonia is a respiratory illness characterized by lung inflammation, often caused by pathogens such as viruses, bacteria, or fungi. Timely detection of pneumonia is crucial for effective treatment. While chest X-rays are commonly used for diagnosis, manual interpretation can be time-consuming, particularly in areas with limited access to trained radiologists. Currently, deep learning models have emerged as an efficient method for pneumonia diagnosis. Numerous researchers are dedicated to enhancing pneumonia diagnostic capabilities through artificial intelligence methods. This study employs a convolutional neural network (CNN) for pneumonia diagnosis. The dataset used in this study consists of chest X-ray images of healthy individuals as well as those affected by bacterial and viral pneumonia. In this study, a CNN model is implemented using an imbalanced chest X-ray dataset with a weighted cross-entropy cost function. The outcome of the developed CNN model shows an accuracy of 75.84%, a precision of 83.16%, a recall of 68.37%, and an F1 score of 68.97% on the test dataset. Further tuning of the model’s hyperparameters is necessary to improve performance metrics.

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A new COVID-19 classification approach based on Bayesian optimization SVM kernel using chest X-ray datasets

Cited by 3 publications

References 50 publications

Intelligent Bayesian Inference for Multiclass Lung Infection Diagnosis: Network Analysis of Ranked Gray Level Co-occurrence (GLCM) Features

Intelligent Bayesian Inference for Multiclass Lung Infection Diagnosis: Network Analysis of Ranked Gray Level Co-occurrence (GLCM) Features

A hybridization of multiple imputation and one-class bagging ensemble approach for missing value and class imbalance problem

Deep Learning-Based Diagnosis of Pneumonia Using Convolutional Neural Networks

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