A unique deep-learning-based model with chest x-ray image for diagnosing COVID-19

Alyaa Mahdi Al-khafagy, Sarah Rafil Hashim, Rusul Ali Enad


Later innovative advancements cleared the way for deep learning-based methods to be used in the therapeutic field due to its exactness for the detection and localization of different illnesses. Recently, the coronavirus widespread has put a parcel of weight on the health framework all around the world. Reverse Transcription- Polymerase Chain Reaction test and medical envisioning are both possible and effective techniques to determine the coronavirus infection. Since coronavirus is highly infection and Reverse Transcription- Polymerase Chain Reaction is time-consuming, determination utilizing a chest X-ray to early diagnosing the infection is considered secure in different situations. A preprocessing step is done first to balance classes inside the dataset and increase the training data. A deep learning-based method is proposed in this study to determine some human lung infections and classify coronavirus from other non-coronavirus diseases accordingly. The proposed model is used for multi-class classification which is more complicated than binary classification especially in the medical image due to the inter classes' large similarity. The proposed procedure effectively classifies four classes that incorporate coronavirus, lung opacity, normal lung, and viral pneumonia with an accuracy of 97.5 %. The proposed strategy appears excellent in terms of accuracy when compared with later strategies.


Chest X-ray; Convolution neural networks; COVID-19; Deep learning; Human lungs

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DOI: http://doi.org/10.11591/ijeecs.v28.i2.pp1147-1154


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