A Systematic Literature Review of Deep Learning Algorithms for Segmentation of the COVID-19 Infection
Source abstract
Abstract
Coronavirus has infected more than 753 million people, ranging in severity from one person to another, where more than six million infected people died worldwide. Computer-aided diagnostic (CAD) with artificial intelligence (AI) showed outstanding performance in effectively diagnosing this virus in real-time. Computed tomography is a complementary diagnostic tool to clarify the damage of COVID-19 in the lungs even before symptoms appear in patients. This paper conducts a systematic literature review of deep learning methods for classifying the segmentation of COVID-19 infection in the lungs. We used the methodology of systematic reviews and meta-analyses (PRISMA) flow method. This research aims to systematically analyze the supervised deep learning methods, open resource datasets, data augmentation methods, and loss functions used for various segment shapes of COVID-19 infection from computerized tomography (CT) chest images. We have selected 56 primary studies relevant to the topic of the paper. We have compared different aspects of the algorithms used to segment infected areas in the CT images. Limitations to deep learning in the segmentation of infected areas still need to be developed to predict smaller regions of infection at the beginning of their appearance.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: COVID-19 diagnosis using AI · Radiomics and Machine Learning in Medical Imaging · AI in cancer detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (98)
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