Paper Title
Survey Paper on Lung Cancer Detection Using CNN
Article Identifiers
Authors
Abhinandan Kurakuri , Dr.S.Nagraj , Hetarth Patel , Altaf Husain , Bestu Singh
Keywords
Lung cancer, Convolutional Neural Networks (CNNs), Medical imageanalysis,Lungcancerdetection,Imagingmodalities,CNNtechniques,Advantages,Limitations,Interpretable AI systems, Lung cancer diagnosis
Abstract
Lung cancer remains a global health crisis, demanding advancements in early and accurate detection methods. Convolutional Neural Networks (CNNs) have emerged as a transformative technology in medical image analysis, holding immense promise for lung cancer detection. This survey comprehensively explores the application of CNNs in this domain. We delve into the fundamentals of CNN architecture, analyze their role in various lung cancer detection tasks using diverse imaging modalities, and conduct a comparative analysis of prominent CNN techniques. The paper further discusses the advantages and limitations of CNN-based approaches, highlighting future research directions that can propel the development of robust and interpretable AI systems for improved lung cancer diagnosis.
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How To Cite (APA)
Abhinandan Kurakuri, Dr.S.Nagraj , Hetarth Patel, Altaf Husain, & Bestu Singh (April-2024). Survey Paper on Lung Cancer Detection Using CNN. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(4), h637-h649. https://ijnrd.org/papers/IJNRD2404767.pdf
Issue
Volume 9 Issue 4, April-2024
Pages : h637-h649
Other Publication Details
Paper Reg. ID: IJNRD_219498
Published Paper Id: IJNRD2404767
Downloads: 000121983
Research Area: Engineering
Country: Ramanagar, Karnataka, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2404767.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2404767
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Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)
ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016
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This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition
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