Deep Learning Models for Classification of COVID-19 Cases by Medical Images
This work addresses the problem of rapid and accurate COVID-19 diagnosis for healthcare systems, but it is incremental as it applies existing deep learning methods to medical image data.
The study tackled the challenge of classifying COVID-19 cases from medical images like CT scans, X-rays, and electrocardiograms by comparing deep transfer learning models (DenseNet201, GoogleNet, AlexNet) against supervised learning models, resulting in enhanced accuracy and speed of diagnosis.
In recent times, the use of chest Computed Tomography (CT) images for detecting coronavirus infections has gained significant attention, owing to their ability to reveal bilateral changes in affected individuals. However, classifying patients from medical images presents a formidable challenge, particularly in identifying such bilateral changes. To tackle this challenge, our study harnesses the power of deep learning models for the precise classification of infected patients. Our research involves a comparative analysis of deep transfer learning-based classification models, including DenseNet201, GoogleNet, and AlexNet, against carefully chosen supervised learning models. Additionally, our work encompasses Covid-19 classification, which involves the identification and differentiation of medical images, such as X-rays and electrocardiograms, that exhibit telltale signs of Covid-19 infection. This comprehensive approach ensures that our models can handle a wide range of medical image types and effectively identify characteristic patterns indicative of Covid-19. By conducting meticulous research and employing advanced deep learning techniques, we have made significant strides in enhancing the accuracy and speed of Covid-19 diagnosis. Our results demonstrate the effectiveness of these models and their potential to make substantial contributions to the global effort to combat COVID-19.