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• W2892854249 abstract "Background: Cancer is a disease which involves the abnormal cell growth that has the potential of dispersal to other parts of the body. Among various conventional anatomical imaging techniques for cancer diagnosis, Magnetic Resonance Imaging (MRI) provides the best spatial resolution and is noninvasive. Current efforts are directed at enhancing the capabilities of MRI in oncology by adding contrast agents. Discussion: Recently, the superior properties of nanomaterials (extremely smaller in size, good biocompatibility and ease in chemical modification) allow its application as a contrast agent for early and specific cancer detection through the MRI. The precise detection of cancer region from any imaging modality will lead to a thriving treatment for cancer patients. The better localization of radiation dose can be attained from MRI by using suitable image processing algorithms. As there are many works that have been proposed for automatic detection for cancers, the effort is also put in to provide an effective survey of Computer Aided Diagnosis (CAD) system for different types of cancer detection with increased efficiency based on recent research works. Even though there are many surveys on MRI contrast agents, they only focused on a particular type of cancer. This study deeply presents the use of nanocontrast agents in MRI for different types of cancer diagnosis. Conclusion: The main aim of this paper is to critically review the available compounds used as nanocontrast agents in MRI modality for different types of cancers. It also includes the review of different methods for cancer cell detection and classification. A comparative analysis is performed to analyze the effect of different CAD systems." @default.
• W2892854249 created "2018-10-05" @default.
• W2892854249 creator A5051652621 @default.
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• W2892854249 date "2019-10-16" @default.
• W2892854249 modified "2023-09-25" @default.
• W2892854249 title "Performance Analysis of Various Nanocontrast Agents and CAD Systems for Cancer Diagnosis" @default.
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• W2892854249 doi "https://doi.org/10.2174/1573405614666180924124736" @default.
• W2892854249 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32008531" @default.
• W2892854249 hasPublicationYear "2019" @default.
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