COVID-19/Pneumonia Classification Based on Guided Attention

Abstract

With the novel coronavirus 19 (COVID-19) continually having a devastating effect around the globe, many scientists and clinicians are actively seeking to develop new techniques to assist with the tackling of this disease. Modern machine learning methods have shown promise in their adoption to assist the health care industry through their data and analytics-driven decision making, inspiring researchers to develop new angles to fight the virus. In this paper, we aim to develop a robust method for the detection of COVID-19 by utilizing patients' chest X-ray images. Despite recent progress, scarcity of data has thus far limited the development of a robust solution. We extend upon existing work by combing publicly available data across 5 different sources and carefully annotating the comprising images into three categories: normal, pneumonia, and COVID-19. To achieve a high classification accuracy, we propose a training pipeline based on the directed guidance of traditional classification networks, where the guidance is directed by an external segmentation network. Through this network, we observed that the widely used, standard networks can achieve an accuracy comparable to tailor-made models specifically for COVID-19, furthermore one network, VGG-16, outperformed the best of the tailor-made models.

A Multi-Task Deep Learning Framework for Simultaneous Detection of Thoracic Pathology through Image Classification

“…Danilov et al [8] have tested four deep learning networks to evaluate the performance for pneumonia, and COVID-19 classification, the networks are Effe-cientNet B1, B2, MobileNetV2, and VGG16 from 2631 CXRs. When they used fine-tuning, the presented model reached an accuracy of 78% which is not a promising value.…”

Comparison Study Of Deep-Learning Architectures For Classification of Thoracic Pathology

“…More than 200 million people around the world are suffering from thoracic diseases [1]. The cause of coronavirus disease-2019 (COVID- 19) is a new virus called sars-CoV-2. Note that pneumonia could be a complication of the COVID_19 disease [2].…”

“…The experimental results of the classification show an accuracy of 97% and 97.8% average sensitivity. Danilov et al [19] have tested four DL networks to evaluate the performance for pneumonia, and COVID-19 classification, the networks are EffecientNet B1, B2, MobileNetV2, and VGG16 from 2631 CXRs. When they used fine-tuning, the presented model reached an accuracy of 78% which is not a promising value.…”

Dynamic Research on Youth Thought, Behavior, and Growth Law Based on Deep Learning Algorithm

“…The ideological and moral qualities, such as world outlook, outlook on life, values and moral sentiment, which are in line with the progress of the times, dominate the purpose and direction of people's behavior choices (Ozaki et al, 2022). As Einstein said, "It is not enough to educate a man with professional knowledge (Danilov et al, 2022). Through professional education, he can become a useful machine, but he cannot become a harmoniously developed man (Koo et al, 2022).…”