Screening Lung Diseases Using Cascaded Feature Generation and Selection Strategies
The global pandemic COVID-19 is still a cause of a health emergency in several parts of the world. Apart from standard testing techniques to identify positive cases, auxiliary tools based on artificial intelligence can help with the identification and containment of the disease. The need for the dev...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Healthcare |
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| Online Access: | https://www.mdpi.com/2227-9032/10/7/1313 |
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| author | Jawad Rasheed Raed M. Shubair |
| author_facet | Jawad Rasheed Raed M. Shubair |
| author_sort | Jawad Rasheed |
| collection | DOAJ |
| description | The global pandemic COVID-19 is still a cause of a health emergency in several parts of the world. Apart from standard testing techniques to identify positive cases, auxiliary tools based on artificial intelligence can help with the identification and containment of the disease. The need for the development of alternative smart diagnostic tools to combat the COVID-19 pandemic has become more urgent. In this study, a smart auxiliary framework based on machine learning (ML) is proposed; it can help medical practitioners in the identification of COVID-19-affected patients, among others with pneumonia and healthy individuals, and can help in monitoring the status of COVID-19 cases using X-ray images. We investigated the application of transfer-learning (TL) networks and various feature-selection techniques for improving the classification accuracy of ML classifiers. Three different TL networks were tested to generate relevant features from images; these TL networks include AlexNet, ResNet101, and SqueezeNet. The generated relevant features were further refined by applying feature-selection methods that include iterative neighborhood component analysis (iNCA), iterative chi-square (iChi2), and iterative maximum relevance–minimum redundancy (iMRMR). Finally, classification was performed using convolutional neural network (CNN), linear discriminant analysis (LDA), and support vector machine (SVM) classifiers. Moreover, the study exploited stationary wavelet (SW) transform to handle the overfitting problem by decomposing each image in the training set up to three levels. Furthermore, it enhanced the dataset, using various operations as data-augmentation techniques, including random rotation, translation, and shear operations. The analysis revealed that the combination of AlexNet, ResNet101, SqueezeNet, iChi2, and SVM was very effective in the classification of X-ray images, producing a classification accuracy of 99.2%. Similarly, AlexNet, ResNet101, and SqueezeNet, along with iChi2 and the proposed CNN network, yielded 99.0% accuracy. The results showed that the cascaded feature generator and selection strategies significantly affected the performance accuracy of the classifier. |
| first_indexed | 2024-03-09T10:17:59Z |
| format | Article |
| id | doaj.art-19bb346605cb44a7be555b4aeb11ce6b |
| institution | Directory Open Access Journal |
| issn | 2227-9032 |
| language | English |
| last_indexed | 2024-03-09T10:17:59Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Healthcare |
| spelling | doaj.art-19bb346605cb44a7be555b4aeb11ce6b2023-12-01T22:12:42ZengMDPI AGHealthcare2227-90322022-07-01107131310.3390/healthcare10071313Screening Lung Diseases Using Cascaded Feature Generation and Selection StrategiesJawad Rasheed0Raed M. Shubair1Department of Software Engineering, Nisantasi University, Istanbul 34398, TurkeyDepartment of Electrical and Computer Engineering, New York University (NYU), Abu Dhabi 129188, United Arab EmiratesThe global pandemic COVID-19 is still a cause of a health emergency in several parts of the world. Apart from standard testing techniques to identify positive cases, auxiliary tools based on artificial intelligence can help with the identification and containment of the disease. The need for the development of alternative smart diagnostic tools to combat the COVID-19 pandemic has become more urgent. In this study, a smart auxiliary framework based on machine learning (ML) is proposed; it can help medical practitioners in the identification of COVID-19-affected patients, among others with pneumonia and healthy individuals, and can help in monitoring the status of COVID-19 cases using X-ray images. We investigated the application of transfer-learning (TL) networks and various feature-selection techniques for improving the classification accuracy of ML classifiers. Three different TL networks were tested to generate relevant features from images; these TL networks include AlexNet, ResNet101, and SqueezeNet. The generated relevant features were further refined by applying feature-selection methods that include iterative neighborhood component analysis (iNCA), iterative chi-square (iChi2), and iterative maximum relevance–minimum redundancy (iMRMR). Finally, classification was performed using convolutional neural network (CNN), linear discriminant analysis (LDA), and support vector machine (SVM) classifiers. Moreover, the study exploited stationary wavelet (SW) transform to handle the overfitting problem by decomposing each image in the training set up to three levels. Furthermore, it enhanced the dataset, using various operations as data-augmentation techniques, including random rotation, translation, and shear operations. The analysis revealed that the combination of AlexNet, ResNet101, SqueezeNet, iChi2, and SVM was very effective in the classification of X-ray images, producing a classification accuracy of 99.2%. Similarly, AlexNet, ResNet101, and SqueezeNet, along with iChi2 and the proposed CNN network, yielded 99.0% accuracy. The results showed that the cascaded feature generator and selection strategies significantly affected the performance accuracy of the classifier.https://www.mdpi.com/2227-9032/10/7/1313COVID-19diagnostic toolpneumoniastationary wavelets transformationtransfer learning |
| spellingShingle | Jawad Rasheed Raed M. Shubair Screening Lung Diseases Using Cascaded Feature Generation and Selection Strategies Healthcare COVID-19 diagnostic tool pneumonia stationary wavelets transformation transfer learning |
| title | Screening Lung Diseases Using Cascaded Feature Generation and Selection Strategies |
| title_full | Screening Lung Diseases Using Cascaded Feature Generation and Selection Strategies |
| title_fullStr | Screening Lung Diseases Using Cascaded Feature Generation and Selection Strategies |
| title_full_unstemmed | Screening Lung Diseases Using Cascaded Feature Generation and Selection Strategies |
| title_short | Screening Lung Diseases Using Cascaded Feature Generation and Selection Strategies |
| title_sort | screening lung diseases using cascaded feature generation and selection strategies |
| topic | COVID-19 diagnostic tool pneumonia stationary wavelets transformation transfer learning |
| url | https://www.mdpi.com/2227-9032/10/7/1313 |
| work_keys_str_mv | AT jawadrasheed screeninglungdiseasesusingcascadedfeaturegenerationandselectionstrategies AT raedmshubair screeninglungdiseasesusingcascadedfeaturegenerationandselectionstrategies |