Detection of COVID-19 from CT Scan Images Using Convolution Neural Network

A subset of machine learning is deep learning, which is only a neural network with three or more layers. Artificial neural networks attempt to replicate the way the human brain works, but they are unable to match it, allowing the computer to “learn” from enormous amounts of data. Deep learning speeds up and simplifies large-scale data interpretation and information production. It is utilized in a variety of fields, including as automatic driving and medical equipment. Some of the data pre-processing that is generally involved with machine learning is eliminated with deep learning. Unstructured text and visual data may be handled by these algorithms, which can also automate feature extraction and lessen the need for human experts. Consider how useful it would be to be able to sort pictures of different pets into categories like “cat,” “dog,” “hamster,” etc. Deep learning algorithms are capable of determining which traits, such as ears, are most essential for distinguishing one species from another. Deep learning algorithms known as convolutional neural networks (CNNs) have been successfully used to picture categorization problems. CNNs have recently been used to recognize COVID-19 from chest X-ray pictures. Based on the presence of distinctive lung features, these models aim to categorize an X-ray image as positive or negative for COVID-19. The models can identify COVID-19 patients with a high degree of accuracy because they were trained on huge datasets of X-ray images. These models could help doctors identify COVID-19 more quickly and precisely, which would ease the strain on healthcare systems during the pandemic.