A Survey on Machine Learning in COVID-19 Diagnosis

Abstract

Since Corona Virus Disease 2019 outbreak, many expert groups worldwide have studied the problem and proposed many diagnostic methods. This paper focuses on the research of Corona Virus Disease 2019 diagnosis. First, the procedure of the diagnosis based on machine learning is introduced in detail, which includes medical data collection, image preprocessing, feature extraction, and image classification. Then, we review seven methods in detail: transfer learning, ensemble learning, unsupervised learning and semi-supervised learning, convolutional neural networks, graph neural networks, explainable deep neural networks, and so on. What's more, the advantages and limitations of different diagnosis methods are compared. Although the great achievements in medical images classification in recent years, Corona Virus Disease 2019 images classification based on machine learning still encountered many problems. For example, the highly unbalanced dataset, the difficulty of collecting labeled data, and the poor quality of the data. Aiming at these problems, we propose some solutions and provide a comprehensive presentation for future research.

Gesture Recognition Based on Deep Learning: A Review

“…Hierarchical representation: CNNs can gradually build hierarchical representation of images through multilayer convolution and pooling operations. This hierarchical representation can help CNNs capture features of different levels in the image, from low-level edges and textures to high-level shapes and structures, to better represent the structure and content of gesture images and improve the accuracy of gesture recognition [33][34][35].…”

Deep Learning Applied to Computational Mechanics: A Comprehensive Review, State of the Art, and the Classics

“…Other Covid-19 machine-learning models. A comprehensive review of machine learning for Covid-19 diagnosis based on medical-data collection, preprocessing of medical images, whose features are extracted, and classified is provided in [341], where methods based on cough sound recordings were not included. Seven methods were reviewed in detail: (1) transfer learning, (2) ensemble learning, (3) unsupervised learning and (4) semi-supervised learning, (5) convolutional neural networks, (6) graph neural networks, (7) explainable deep neural networks.…”

“…The use of deep learning as one of several machine-learning techniques for Covid-19 diagnosis was reviewed in [341] [342] [343], as mentioned above.…”

Recent Machine Learning and Deep Learning Theories and Methods for COVID-19 Diagnosis

“…Although AI tools like machine learning and deep learning can make quick decisions for the diagnosis of the infection of COVID-19 using medical images [44], using machine learning and deep learning in COVID-19 diagnosis presents several challenges. Firstly, obtaining high-quality labeled data for training these models can be difficult [45]. Building robust datasets that include diverse patient populations, different disease stages, and reliable ground truth labels can be time-consuming and resource-intensive.…”