Classification of COVID-19 from tuberculosis and pneumonia using deep learning techniques.
Deep learning provides the healthcare industry with the ability to analyse data at exceptional speeds without compromising on accuracy. These techniques are applicable to healthcare domain for accurate and timely prediction. Convolutional neural network is a class of deep learning methods which has...
| Published in: | Medical & Biological Engineering & Computing Vol. 60; no. 9; pp. 2681 - 2692 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
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Springer Nature
Sep2022
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=158447336&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 158447336 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Sep2022 vid: 60 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 158447336 157974868 158447336 NLM35834050 10.1007/s11517-022-02632-x NLM35834050 158447336 ppf: 2681 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Classification of COVID-19 from tuberculosis and pneumonia using deep learning techniques. aug: au: Venkataramana, Lokeswari Prasad, D. Venkata Vara Saraswathi, S. Mithumary, C. M. Karthikeyan, R. Monika, N. affil: Department of CSE, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, Chennai, India sug: ab: Deep learning provides the healthcare industry with the ability to analyse data at exceptional speeds without compromising on accuracy. These techniques are applicable to healthcare domain for accurate and timely prediction. Convolutional neural network is a class of deep learning methods which has become dominant in various computer vision tasks and is attracting interest across a variety of domains, including radiology. Lung diseases such as tuberculosis (TB), bacterial and viral pneumonias, and COVID-19 are not predicted accurately due to availability of very few samples for either of the lung diseases. The disease could be easily diagnosed using X-ray or CT scan images. But the number of images available for each of the disease is not as equally as other resulting in imbalance nature of input data. Conventional supervised machine learning methods do not achieve higher accuracy when trained using a lesser amount of COVID-19 data samples. Image data augmentation is a technique that can be used to artificially expand the size of a training dataset by creating modified versions of images in the dataset. Data augmentation helped reduce overfitting when training a deep neural network. The SMOTE (Synthetic Minority Oversampling Technique) algorithm is used for the purpose of balancing the classes. The novelty in this research work is to apply combined data augmentation and class balance techniques before classification of tuberculosis, pneumonia, and COVID-19. The classification accuracy obtained with the proposed multi-level classification after training the model is recorded as 97.4% for TB and pneumonia and 88% for bacterial, viral, and COVID-19 classifications. The proposed multi-level classification method produced is ~8 to ~10% improvement in classification accuracy when compared with the existing methods in this area of research. The results reveal the fact that the proposed system is scalable to growing medical data and classifies lung diseases and its sub-types in less time with higher accuracy. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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