Classification of COVID-19 and Pneumonia Using Deep Transfer Learning.

The World Health Organization (WHO) recognized COVID-19 as the cause of a global pandemic in 2019. COVID-19 is caused by SARS-CoV-2, which was identified in China in late December 2019 and is indeed referred to as the severe acute respiratory syndrome coronavirus-2. The whole globe was hit within several months. As millions of individuals around the world are infected with COVID-19, it has become a global health concern. The disease is usually contagious, and those who are infected can quickly pass it on to others with whom they come into contact. As a result, monitoring is an effective way to stop the virus from spreading further. Another disease caused by a virus similar to COVID-19 is pneumonia. The severity of pneumonia can range from minor to life-threatening. This is particularly hazardous for children, people over 65 years of age, and those with health problems or immune systems that are affected. In this paper, we have classified COVID-19 and pneumonia using deep transfer learning. Because there has been extensive research on this subject, the developed method concentrates on boosting precision and employs a transfer learning technique as well as a model that is custom-made. Different pretrained deep convolutional neural network (CNN) models were used to extract deep features. The classification accuracy was used to measure performance to a great extent. According to the findings of this study, deep transfer learning can detect COVID-19 and pneumonia from CXR images. Pretrained customized models such as MobileNetV2 had a 98% accuracy, InceptionV3 had a 96.92% accuracy, EffNet threshold had a 94.95% accuracy, and VGG19 had a 92.82% accuracy. MobileNetV2 has the best accuracy of all of these models.

A Novel and Robust Approach to Detect Tuberculosis Using Transfer Learning.

Deep learning has emerged as a promising technique for a variety of elements of infectious disease monitoring and detection, including tuberculosis. We built a deep convolutional neural network (CNN) model to assess the generalizability of the deep learning model using a publicly accessible tuberculosis dataset. This study was able to reliably detect tuberculosis (TB) from chest X-ray images by utilizing image preprocessing, data augmentation, and deep learning classification techniques. Four distinct deep CNNs (Xception, InceptionV3, InceptionResNetV2, and MobileNetV2) were trained, validated, and evaluated for the classification of tuberculosis and nontuberculosis cases using transfer learning from their pretrained starting weights. With an F1-score of 99 percent, InceptionResNetV2 had the highest accuracy. This research is more accurate than earlier published work. Additionally, it outperforms all other models in terms of reliability. The suggested approach, with its state-of-the-art performance, may be helpful for computer-assisted rapid TB detection.

Study on Convolutional Neural Network to Detect COVID-19 from Chest X-Rays

The world is facing a pandemic due to the coronavirus disease 2019 (COVID-19), named as per the World Health Organization. COVID-19 is caused by the virus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which was initially discovered in late December 2019 in Wuhan, China. Later, the virus had spread throughout the world within a few months. COVID-19 has become a global health crisis because millions of people worldwide are affected by this fatal virus. Fever, dry cough, and gastrointestinal problems are the most common signs of COVID-19. The disease is highly contagious, and affected people can easily spread the virus to those with whom they have close contact. Thus, contact tracing is a suitable solution to prevent the virus from spreading. The method of identifying all persons with whom a COVID-19-affected patient has come into contact in the last 2 weeks is called contact tracing. This study presents an investigation of a convolutional neural network (CNN), which makes the test faster and more reliable, to detect COVID-19 from chest X-ray (CXR) images. Because there are many studies in this field, the designed model focuses on increasing the accuracy level and uses a transfer learning approach and a custom model. Pretrained deep CNN models, such as VGG16, InceptionV3, MobileNetV2, and ResNet50, have been used for deep feature extraction. The performance measurement in this study was based on classification accuracy. The results of this study indicate that deep learning can recognize SARS-CoV-2 from CXR images. The designed model provided 93% accuracy and 98% validation accuracy, and the pretrained customized models such as MobileNetV2 obtained 97% accuracy, InceptionV3 obtained 98%, and VGG16 obtained 98% accuracy, respectively. Among these models, InceptionV3 has recorded the highest accuracy. [ABSTRACT FROM AUTHOR] Copyright of Mathematical Problems in Engineering is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Deep Learning Approaches for Detecting Pneumonia in COVID-19 Patients by Analyzing Chest X-Ray Images

The COVID-19 pandemic has wreaked havoc in the daily life of human beings and devastated many economies worldwide, claiming millions of lives so far. Studies on COVID-19 have shown that older adults and people with a history of various medical issues, specifically prior cases of pneumonia, are at a higher risk of developing severe complications from COVID-19. As pneumonia is a common type of infection that spreads in the lungs, doctors usually perform chest X-ray to identify the infected regions of the lungs. In this study, machine learning tools such as LabelBinarizer are used to perform one-hot encoding on the labeled chest X-ray images and transform them into categorical form using Python's to_categorical tool. Subsequently, various deep learning features such as convolutional neural network (CNN), VGG16, AveragePooling2D, dropout, flatten, dense, and input are used to build a detection model. Adam is used as an optimizer, which can be further applied to predict pneumonia in COVID-19 patients. The model predicted pneumonia with an average accuracy of 91.69%, sensitivity of 95.92%, and specificity of 100%. The model also efficiently reduces training loss and increases accuracy. [ABSTRACT FROM AUTHOR] Copyright of Mathematical Problems in Engineering is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)