This article presents a systematic overview of artificial intelligence (AI) and computer vision strategies for diagnosing the coronavirus disease of 2019 (COVID-19) using computerized tomography (CT) medical images. We analyzed the previous review works and found that all of them ignored classifying and categorizing COVID-19 literature based on computer vision tasks, such as classification, segmentation, and detection. Most of the COVID-19 CT diagnosis methods comprehensively use segmentation and classification tasks. Moreover, most of the review articles are diverse and cover CT as well as X-ray images. Therefore, we focused on the COVID-19 diagnostic methods based on CT images. Well-known search engines and databases such as Google, Google Scholar, Kaggle, Baidu, IEEE Xplore, Web of Science, PubMed, ScienceDirect, and Scopus were utilized to collect relevant studies. After deep analysis, we collected 114 studies and reported highly enriched information for each selected research. According to our analysis, AI and computer vision have substantial potential for rapid COVID-19 diagnosis as they could significantly assist in automating the diagnosis process. Accurate and efficient models will have real-time clinical implications, though further research is still required. Categorization of literature based on computer vision tasks could be helpful for future research; therefore, this review article will provide a good foundation for conducting such research.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8684223 | PMC |
| http://dx.doi.org/10.1016/j.compbiomed.2021.105123 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Humans excel at applying learned behavior to unlearned situations. A crucial component of this generalization behavior is our ability to compose/decompose a whole into reusable parts, an attribute known as compositionality. One of the fundamental questions in robotics concerns this characteristic: How can linguistic compositionality be developed concomitantly with sensorimotor skills through associative learning, particularly when individuals only learn partial linguistic compositions and their corresponding sensorimotor patterns? To address this question, we propose a brain-inspired neural network model that integrates vision, proprioception, and language into a framework of predictive coding and active inference on the basis of the free-energy principle.
View Article and Find Full Text PDFCDCG-UNet: Chaotic Optimization Assisted Brain Tumor Segmentation Based on Dilated Channel Gate Attention U-Net Model.
Neuroinformatics
January 2025
Department of Information Technology, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Ramapuram, Chennai, 600089, India.
Brain tumours are one of the most deadly and noticeable types of cancer, affecting both children and adults. One of the major drawbacks in brain tumour identification is the late diagnosis and high cost of brain tumour-detecting devices. Most existing approaches use ML algorithms to address problems, but they have drawbacks such as low accuracy, high loss, and high computing cost.
View Article and Find Full Text PDFBDMANGO: An image dataset for identifying the variety of mango based on the mango leaves.
Data Brief
February 2025
Department of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, Bangladesh.
In the field of agriculture, particularly within the context of machine learning applications, quality datasets are essential for advancing research and development. To address the challenges of identifying different mango leaf types and recognizing the diverse and unique characteristics of mango varieties in Bangladesh, a comprehensive and publicly accessible dataset titled "BDMANGO" has been created. This dataset includes images essential for research, featuring six mango varieties: Amrapali, Banana, Chaunsa, Fazli, Haribhanga, and Himsagar, which were collected from different locations.
View Article and Find Full Text PDFRadiomics model based on computed tomography images for prediction of radiation-induced optic neuropathy following radiotherapy of brain and head and neck tumors.
Heliyon
January 2025
Department of Optometry and Vision Science, School of Rehabilitation, Tehran University of Medical Science, Tehran, Iran.
Purpose: We aimed to build a machine learning-based model to predict radiation-induced optic neuropathy in patients who had treated head and neck cancers with radiotherapy.
Materials And Methods: To measure radiation-induced optic neuropathy, the visual evoked potential values were obtained in both case and control groups and compared. Radiomics features were extracted from the area segmented which included the right and left optic nerves and chiasm.
Metastasis lesion segmentation from bone scintigrams using encoder-decoder architecture model with multi-attention and multi-scale learning.
Quant Imaging Med Surg
January 2025
School of Computing, Mathematics and Engineering, Charles Sturt University, Albury, Australia.
Background: The limitation in spatial resolution of bone scintigraphy, combined with the vast variations in size, location, and intensity of bone metastasis (BM) lesions, poses challenges for accurate diagnosis by human experts. Deep learning-based analysis has emerged as a preferred approach for automating the identification and delineation of BM lesions. This study aims to develop a deep learning-based approach to automatically segment bone scintigrams for improving diagnostic accuracy.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!