Documentdetail
ID kaart
oai:arXiv.org:2408.13315
Onderwerp
Electrical Engineering and Systems... Computer Science - Computer Vision... I.4.0 I.5.0Auteur
Xu, XinmeiCategorie
Computer Science
Jaar
2024
vermelding datum
28-08-2024
Trefwoorden
detection paper research review pneumonia deep methodsBeschrijving
Pneumonia disease is one of the leading causes of death among children and adults worldwide.
In the last ten years, computer-aided pneumonia detection methods have been developed to improve the efficiency and accuracy of the diagnosis process.
Among those methods, the effects of deep learning approaches surpassed that of other traditional machine learning methods.
This review paper searched and examined existing mainstream deep-learning approaches in the detection of pneumonia regions.
This paper focuses on key aspects of the collected research, including their datasets, data processing techniques, general workflow, outcomes, advantages, and limitations.
This paper also discusses current challenges in the field and proposes future work that can be done to enhance research procedures and the overall performance of deep learning models in detecting, classifying, and localizing infected regions.
This review aims to offer an insightful summary and analysis of current research, facilitating the development of deep learning approaches in addressing treatable diseases.
;Comment: 8 pages, 1 figure, published on Applied and Computational Engineering
Xu, Xinmei, 2024, A systematic review: Deep learning-based methods for pneumonia region detection
Analysis of the ICML 2023 Ranking Data: Can Authors' Opinions of Their Own Papers Assist Peer Review in Machine Learning?
peer author-provided analysis rankings scores review learning machine science computer
