INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
Lung cancer stands as one of the most pervasive and deadliest cancer types on a global scale. Detecting lung cancer at an early stage can significantly heighten the
likelihood of successful treatment and improved patient outcomes. Over the past years, image processing techniques have emerged as promising tools for facilitating early lung cancer detection. This paper presents a comprehensive review of the image processing techniques employed in lung cancer detection. The study delves into the diverse modalities of medical imaging, including X-rays, CT scans, and MRI, and explores the image processing techniques implemented for feature extraction and classification. The review accentuates the importance of employing image processing techniques for lung cancer detection, as they enable the identification of subtle changes in lung tissue that may elude the human eye. Additionally, the review emphasizes the pressing need for developing more accurate and robust image processing techniques to enhance early detection and treatment of lung cancer. In conclusion, the utilization of image processing techniques for lung cancer detection has demonstrated promise in recent times. This review sheds light on the potential benefits of leveraging these techniques to facilitate early detection and treatment of lung cancer, and highlights the importance of continued research and advancement in this field. Lung cancer is a prevalent and highly lethal form of cancer that affects populations.
Keywords:
Keywords: Cancer Detection; Image processing; Feature extraction; Enhancement Watershed Masking.
Cite Article:
"Lung Cancer Detection System Using based on deep learning", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 5, page no.h211-h217, May-2023, Available :http://www.ijnrd.org/papers/IJNRD2305726.pdf
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ISSN:
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
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