Classification and Detection of COVID-19 and Other Chest-Related Diseases Using Transfer Learning

Classification and Detection of COVID-19 and Other Chest-Related Diseases Using Transfer Learning

Blog Article

COVID-19 has infected millions of people worldwide over the past few years.The main technique used for COVID-19 detection is reverse transcription, which is expensive, sensitive, and requires medical expertise.X-ray imaging is an alternative hp pavilion 15-eg1053cl and more accessible technique.

This study aimed to improve detection accuracy to create a computer-aided diagnostic tool.Combining other artificial intelligence applications techniques with radiological imaging can help detect different diseases.This study proposes a technique for the automatic detection of COVID-19 and other chest-related diseases using digital chest X-ray images of suspected patients by applying transfer learning (TL) algorithms.

For this purpose, two balanced datasets, Dataset-1 and Dataset-2, were created by combining four public databases and collecting images from recently published articles.Dataset-1 consisted of 6000 chest X-ray images with 1500 for each class.Dataset-2 consisted of 7200 images with 1200 for each class.

To train and test the model, TL with nine pretrained convolutional neural networks (CNNs) was used with augmentation as a preprocessing method.The network was trained to classify using five classifiers: two-class classifier (normal and COVID-19); three-class classifier (normal, COVID-19, and viral pneumonia), four-class classifier (normal, viral pneumonia, COVID-19, and tuberculosis (Tb)), five-class classifier (normal, bacterial pneumonia, COVID-19, Tb, and pneumothorax), and six-class classifier (normal, bacterial pneumonia, COVID-19, viral pneumonia, Tb, and pneumothorax).For two, three, four, five, and six classes, our here model achieved a maximum accuracy of 99.

83, 98.11, 97.00, 94.

66, and 87.29%, respectively.